Search

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE., SNAC

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier., Peer Reviewed, Postprint (published version)

Measuring the depth-of-interaction (DOI) of gamma photons enables increasing the resolution of emission imaging systems. Several design variants of DOI-sensitive detectors have been recently introduced to improve the performance of scanners for positron emission tomography (PET). However, the accurate characterization of the response of DOI detectors, necessary to accurately measure the DOI, remains an unsolved problem. Numerical simulations are, at the state of the art, imprecise, while measuring directly the characteristics of DOI detectors experimentally is hindered by the impossibility to impose the depth-of-interaction in an experimental set-up. In this article we introduce a machine learning approach for extracting accurate forward models of gamma imaging devices from simple pencil-beam measurements, using a nonlinear dimensionality reduction technique in combination with a finite mixture model. The method is purely data-driven, not requiring simulations, and is applicable to a wide range of detector types. The proposed method was evaluated both in a simulation study and with data acquired using a monolithic gamma camera designed for PET (the cMiCE detector), demonstrating the accurate recovery of the DOI characteristics. The combination of the proposed calibration technique with maximum-a posteriori estimation of the coordinates of interaction provided a depth resolution of approximate to 1.14 mm for the simulated PET detector and approximate to 1.74 mm for the cMiCE detector. The software and experimental data are made available at http://occiput.mgh.harvard.edu/depthembedding/.

Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with the following diagnoses: cognitive normal (CN; n=137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found that classifications using embedded MRI features generally outperformed (p<0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p=0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: =0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvemen

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

Isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE) are recently proposed nonlinear dimensionality reduction methods of manifolds. When these methods are satisfied with some specific constraints, some hidden connections can be found between principal component analysis (PCA) and those manifolds learning based approaches. In this paper, some derivations are presented to validate the idea and then some conclusions are drawn. © 2012 Springer-Verlag.

Isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE) are recently proposed nonlinear dimensionality reduction methods of manifolds. When these methods are satisfied with some specific constraints, some hidden connections can be found between principal component analysis (PCA) and those manifolds learning based approaches. In this paper, some derivations are presented to validate the idea and then some conclusions are drawn. © 2012 Springer-Verlag.

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

Isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE) are recently proposed nonlinear dimensionality reduction methods of manifolds. When these methods are satisfied with some specific constraints, some hidden connections can be found between principal component analysis (PCA) and those manifolds learning based approaches. In this paper, some derivations are presented to validate the idea and then some conclusions are drawn. © 2012 Springer-Verlag.

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facia, Taktil Video

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facia, Taktil Video

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

In this paper, a comparison of methods for nonlinear dimensionality reduction is proposed in order to determine which technique preserves better the local properties, without losing the overall structure of the original data. We seek to establish which of these methods is the most appropriate for visualization tasks. The embeddings obtained with each technique are evaluated by two criteria Preservation Neighborhood Error and Preserved Neighbors Average. The methodologies were tested on artificial and real-world data sets which allow us to visually confirm the quality of the embedding. The results obtained show that Maximum variance unfolding computes high quality embeddings, because the optimization problem pretends to preserve exactly the local pair-wise distance between neighbors and conserve the global manifold structure., En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facia, Taktil Video

In this paper, a comparison of methods for nonlinear dimensionality reduction is proposed in order to determine which technique preserves better the local properties, without losing the overall structure of the original data. We seek to establish which of these methods is the most appropriate for visualization tasks. The embeddings obtained with each technique are evaluated by two criteria Preservation Neighborhood Error and Preserved Neighbors Average. The methodologies were tested on artificial and real-world data sets which allow us to visually confirm the quality of the embedding. The results obtained show that Maximum variance unfolding computes high quality embeddings, because the optimization problem pretends to preserve exactly the local pair-wise distance between neighbors and conserve the global manifold structure., En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facia, Taktil Video

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facia, Taktil Video

A challenging goal for both the game industry and the research community of computer graphics is the generation of 3D virtual avatars that automatically perform realistic human motions with high speed at low monetary cost. So far, full body motion estimation of human complexity remains an important open problem. We propose a realistic motion estimation framework to control the animation of 3D avatars. Instead of relying on a motion capture device as the control signal, we use low-cost and ubiquitously available 3D accelerometer sensors. The framework is developed in a data-driven fashion, which includes two phases: model learning from an existing high quality motion database, and motion synthesis from the control signal. In the phase of model learning, we built a high quality motion model of less complexity that learned from a large motion capture database. Then, by taking the 3D accelerometer sensor signal as input, we were able to synthesize high-quality motion from the motion model we learned. In this thesis, we present two different techniques for model learning and motion synthesis, respectively. Linear and nonlinear reduction techniques for data dimensionality are applied to search for the proper low dimensional representation of motion data. Two motion synthesis methods, interpolation and optimization, are compared using the 3D acceleration signals with high noise. We evaluate the result visually compared to the real video and quantitatively compared to the ground truth motion. The system performs well, which makes it available to a wide range of interactive applications, such as character control in 3D virtual environments and occupational training.

Facial expressions play an important role in every day social interaction. To enhance the daily life experience for the visually impaired, we present the Facial Expression Appearance vibroTactile System (FEATS), which uses a vibrotactile chair as the social interface for the visually impaired. An array of vibrating motors are mounted spatially on the back of an office chair. The Locally Linear Embedding (LLE) algorithm is extended to compute the manifold of facial expressions, which is used to control vibration of motors to render emotions. Thus, the chair could provide the visually impaired with on-line dynamic emotion information about the person he/she is communicating with. Usability evaluation of the system is carried out. The results are encouraging and demonstrate usability for the visually impaired. The user studies show that perfect recognition accuracy of emotion type is achieved by the FEATS.

Text mining is the extraction of important information from a collection of textual data sources. For instance, text mining can be used to discover related concepts or to categorize previously unseen documents. In this age of information overload, text mining applications can potentially yield tremendous benefits to both individuals and organizations. However, the effectiveness of text mining is limited by the large volume of textual data, as well as its complex and noisy characteristics. Both of these challenges can be addressed with "dimensionality reduction" (DR). DR is the process of transforming a large amount of data into a much smaller, less noisy representation that preserves important relationships from the original data. DR techniques have been shown to effectively simplify large geometric datasets, but have yet to be adequately evaluated for textual data. This project evaluated five DR techniques (Principal Components Analysis, Multidimensional Scaling, Isomap, Locally Linear Embedding, and Laplace-Beltrami Diffusion Maps) from two distinct perspectives. First, the impact of each DR technique on the ability to automatically perform document classification on corpuses of scientific abstracts or news articles was measured. For each technique, the dataset was reduced, then a standard linear, quadratic, or nearest neighbor classifier was used to assign categories to a test set of documents based upon a labeled training set. Results showed that, for any fixed number of dimensions used by the classifier, performing any kind of DR almost always improved classification accuracy compared to using the non-reduced data. Amongst different DR techniques, Isomap and Multi-dimensional Scaling were best able to reduce the data and eliminate noise, yielding improved accuracy. This suggests that these textual data sets lie primarily on a linear manifold for which the more complex non-linear techniques do not have an advantage., The original document contains color images.

Non-linear dimensionality reduction and vector segmentation of hyper-spectral images is investigated in this letter. The proposed framework takes into account the nonlinear nature of high dimensional hyper-spectral images, and projects onto a lower dimensional space via a spatially-coherent locally linear embedding technique. The spatial coherence is introduced by comparing individual pixels based on their local surrounding neighborhood structure. This neighborhood concept is also extended to the segmentation and classification stages using a modified vector angle distance. We present the underlying concepts of the proposed framework and experimental results showing the significant classification improvements., Sponsored in part by the Office of Naval Research, Defense Advanced Research Projects Agency and National Science Foundation.

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Raw data sets taken with various capturing devices are usually multidimensional and need to be preprocessed before applying subsequent operations, such as clustering, classification, outlier detection, noise filtering etc. One of the steps of data preprocessing is dimensionality reduction. It has been developed with an aim to reduce or eliminate information bearing secondary importance, and retain or highlight meaningful information while reducing the dimensionality of data. Since the nature of real-world data is often nonlinear, linear dimensionality reduction techniques, such as principal component analysis (PCA), fail to preserve a structure and relationships in a highdimensional space when data are mapped into a low-dimensional space. This means that nonlinear dimensionality reduction methods are in demand in this case. Among them is a method called locally linear embedding (LLE), which is the focus of this thesis. Its main attractive characteristics are few free parameters to be set and a non-iterative solution avoiding the convergence to a local minimum. In this thesis, several extensions to the conventional LLE are proposed, which aid us to overcome some limitations of the algorithm. The study presents a comparison between LLE and three nonlinear dimensionality reduction techniques (isometric feature mapping (Isomap), self-organizing map (SOM) and fast manifold learning based on Riemannian normal coordinates (S-LogMap) applied to manifold learning. This comparison is of interest, since all of the listed methods reduce high-dimensional data in different ways, and it is worth knowing for which case a particular method outperforms others. A number of applications of dimensionality reduction techniques exist in data mining. One of them is visualization of high-dimensional data sets. The main goal of data visualization is to find a one, two or three-dimensional descriptive data projection, which captures and highlights important knowledge about data while

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

In this paper we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold learning methods, particularly locally linear embedding (LLE). Specifically, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is veryflexible and gives good empirical results.

In this paper we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold learning methods, particularly locally linear embedding (LLE). Specifically, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is veryflexible and gives good empirical results.

In this paper we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold learning methods, particularly locally linear embedding (LLE). Specifically, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is veryflexible and gives good empirical results.