34 results on '"regularized discriminant analysis"'
Search Results
2. Previsual and Early Detection of Myrtle Rust on Rose Apple Using Indices Derived from Thermal Imagery and Visible-to-Short-Infrared Spectroscopy.
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Watt, Michael S., Bartlett, Michael, Soewarto, Julia, de Silva, Dilshan, Estarija, Honey Jane C., Massam, Peter, Cajes, David, Yorston, Warren, Graevskaya, Elizaveta, Dobbie, Kiryn, Fraser, Stuart, Dungey, Heidi S., and Buddenbaum, Henning
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MYRTLE (Plants) , *EARLY diagnosis , *SPECTROMETRY , *ROSES , *DISEASE progression - Abstract
Myrtle rust, caused by the fungus Austmpuccinia psidii, is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jumbos') that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (Fl score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatai conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (Fl score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
3. Adaptive Data Structure Regularized Multiclass Discriminative Feature Selection.
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Fan, Mingyu, Zhang, Xiaoqin, Hu, Jie, Gu, Nannan, and Tao, Dacheng
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FEATURE selection , *SMART structures , *DATA distribution , *SUPERVISED learning , *DATA structures - Abstract
Feature selection (FS), which aims to identify the most informative subset of input features, is an important approach to dimensionality reduction. In this article, a novel FS framework is proposed for both unsupervised and semisupervised scenarios. To make efficient use of data distribution to evaluate features, the framework combines data structure learning (as referred to as data distribution modeling) and FS in a unified formulation such that the data structure learning improves the results of FS and vice versa. Moreover, two types of data structures, namely the soft and hard data structures, are learned and used in the proposed FS framework. The soft data structure refers to the pairwise weights among data samples, and the hard data structure refers to the estimated labels obtained from clustering or semisupervised classification. Both of these data structures are naturally formulated as regularization terms in the proposed framework. In the optimization process, the soft and hard data structures are learned from data represented by the selected features, and then, the most informative features are reselected by referring to the data structures. In this way, the framework uses the interactions between data structure learning and FS to select the most discriminative and informative features. Following the proposed framework, a new semisupervised FS (SSFS) method is derived and studied in depth. Experiments on real-world data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2022
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4. Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks.
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Fu, Rongrong, Han, Mengmeng, Bao, Tiantian, Wang, Fuwang, and Shi, Peiming
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OBJECT manipulation , *PSYCHOLOGICAL feedback , *TOPOGRAPHIC maps , *ELECTROENCEPHALOGRAPHY , *DISCRIMINANT analysis , *TASKS - Abstract
Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is ∼ 0. 9 4. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts. [ABSTRACT FROM AUTHOR]
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- 2021
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5. Classification of Tactile and Motor Velocity-Evoked Hemodynamic Response in Primary Somatosensory and Motor Cortices as Measured by Functional Near-Infrared Spectroscopy.
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Hozan, Mohsen, Greenwood, Jacob, Sullivan, Michaela, and Barlow, Steven
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HEMODYNAMICS ,SOMATOSENSORY cortex ,MOTOR cortex ,NEAR infrared spectroscopy ,SPECTROMETRY ,FEATURE extraction ,TIME series analysis - Abstract
Functional near-infrared spectroscopy (fNIRS) is an emerging technique in studying cerebral hemodynamics; however, consensus on the analysis methods and the clinical applications has yet to be established. In this study, we demonstrate the results of a pilot fNIRS study of cerebral hemodynamic response (HR) evoked by pneumotactile and sensorimotor stimuli on the dominant hand. Our goal is to find the optimal stimulus parameters to maximally evoke HR in the primary somatosensory and motor cortices. We use a pulsatile pneumatic array of 14 tactile cells that were attached to the glabrous surface of the dominant hand, with a patterned stimulus that resembles saltation at three distinct traverse velocities [10, 25, and 45 cm/s]. NIRS optodes (16 sources; 20 detectors) are bilaterally and symmetrically placed over the pre-and post-central gyri (M1 and S1). Our objective is to identify the extent to which cerebral HR can encode the velocity of the somatosensory and/or motor stimuli. We use common spatial pattern for feature extraction and regularized-discriminant analysis for classifying the fNIRS time series into velocity classes. The classification results demonstrate discriminatory features of the fNIRS signal from each distinct stimulus velocity. The results are inconclusive regarding the velocity which evokes the highest intensity of hemodynamic response. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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6. Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition
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Tomasz Krzeszowski and Krzysztof Wiktorowicz
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gait recognition ,biometrics ,regularized discriminant analysis ,particle swarm optimization ,grey wolf optimization ,whale optimization algorithm ,Chemical technology ,TP1-1185 - Abstract
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.
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- 2020
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7. Intelligent Sensors for Human Motion Analysis.
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Krzeszowski, Tomasz, Calafate, Carlos Tavares, Kepski, Michal, Krzeszowski, Tomasz, and Świtoński, Adam
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History of engineering & technology ,Technology: general issues ,3D human mesh reconstruction ,3D human pose estimation ,3D multi-person pose estimation ,Azure Kinect ,BILSTM ,Berg Balance Scale ,COVID-19 ,EMG ,F-Formation ,FFNN ,FMCW ,GRU ,Kinect v2 ,LSTM ,MFCC ,RGB-D sensors ,XGBoost ,Zed 2i ,absolute poses ,action units ,aggregation function ,anomaly detection ,artifact classification ,artifact detection ,artificial intelligence ,assessment ,balance ,biometrics ,camera-centric coordinates ,computer vision ,convolutional neural networks ,cyber-physical systems ,data augmentation ,deep learning ,deep neural network ,deep-learning ,development ,diagnosis ,elderly ,facial expression recognition ,facial landmarks ,fall risk detection ,features fusion ,features selection ,fuzzy inference ,gait analysis ,gait parameters ,gait recognition ,gap filling ,generalization ,graph convolutional networks ,grey wolf optimization ,human action recognition ,human motion analysis ,human motion modelling ,human tracking ,kinematics ,knowledge measure ,machine learning ,markerless ,markerless motion capture ,modular sensing unit ,motion capture ,movement tracking ,n/a ,neural networks ,optical sensing principle ,particle swarm optimization ,pattern recognition ,plantar pressure measurement ,pose estimation ,posture detection ,precedence indicator ,recognition ,reconstruction ,regularized discriminant analysis ,robot ,rule induction ,skeletal data ,socially occupied space ,telemedicine ,time series classification ,vital sign ,whale optimization algorithm - Abstract
Summary: The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.
8. Structures of the covariance matrices in the classifier design
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Raudys, Šarūnas, Saudargiene, Aušra, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Amin, Adnan, editor, Dori, Dov, editor, Pudil, Pavel, editor, and Freeman, Herbert, editor
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- 1998
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9. Coupled regularized sample covariance matrix estimator for multiple classes
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Esa Ollila, Elias Raninen, Dept Signal Process and Acoust, Aalto-yliopisto, and Aalto University
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FOS: Computer and information sciences ,Optimization ,Computer Science - Machine Learning ,Mean squared error ,Covariance matrices ,Portfolios ,Population ,Identity matrix ,Machine Learning (stat.ML) ,02 engineering and technology ,Positive-definite matrix ,Tuning ,regularized discriminant analysis ,Machine Learning (cs.LG) ,elliptical distribution ,Methodology (stat.ME) ,Sociology ,Statistics - Machine Learning ,0202 electrical engineering, electronic engineering, information engineering ,Applied mathematics ,Electrical and Electronic Engineering ,education ,Statistics - Methodology ,education.field_of_study ,Statistics ,Estimator ,020206 networking & telecommunications ,Covariance matrix estimation ,Covariance ,Linear discriminant analysis ,regularization ,shrinkage ,Signal Processing ,Estimation ,Elliptical distribution - Abstract
Publisher Copyright: Author The estimation of covariance matrices of multiple classes with limited training data is a difficult problem. The sample covariance matrix (SCM) is known to perform poorly when the number of variables is large compared to the available number of samples. In order to reduce the mean squared error (MSE) of the SCM, regularized (shrinkage) SCM estimators are often used. In this work, we consider regularized SCM (RSCM) estimators for multiclass problems that couple together two different target matrices for regularization: the pooled (average) SCM of the classes and the scaled identity matrix. Regularization toward the pooled SCM is beneficial when the population covariances are similar, whereas regularization toward the identity matrix guarantees that the estimators are positive definite. We derive the MSE optimal tuning parameters for the estimators as well as propose a method for their estimation under the assumption that the class populations follow (unspecified) elliptical distributions with finite fourth-order moments. The MSE performance of the proposed coupled RSCMs are evaluated with simulations and in a regularized discriminant analysis (RDA) classification set-up on real data. The results based on three different real data sets indicate comparable performance to cross-validation but with a significant speed-up in computation time.
- Published
- 2020
10. Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition
- Author
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Krzysztof Wiktorowicz and Tomasz Krzeszowski
- Subjects
biometrics ,Computer science ,Intelligence ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,lcsh:Chemical technology ,Biochemistry ,Swarm intelligence ,Article ,regularized discriminant analysis ,Analytical Chemistry ,gait recognition ,Gait (human) ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,whale optimization algorithm ,Instrumentation ,Gait ,grey wolf optimization ,Hyperparameter ,particle swarm optimization ,business.industry ,Particle swarm optimization ,Discriminant Analysis ,020207 software engineering ,Pattern recognition ,Linear discriminant analysis ,Atomic and Molecular Physics, and Optics ,ComputingMethodologies_PATTERNRECOGNITION ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithms - Abstract
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.
- Published
- 2020
11. Classification of Tactile and Motor Velocity-Evoked Hemodynamic Response in Primary Somatosensory and Motor Cortices as Measured by Functional Near-Infrared Spectroscopy
- Author
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Jacob Greenwood, Steven M. Barlow, Mohsen Hozan, and Michaela Sullivan
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common spatial pattern ,Haemodynamic response ,Pulsatile flow ,fNIRS ,Stimulus (physiology) ,Somatosensory system ,lcsh:Technology ,regularized discriminant analysis ,somatosensory ,lcsh:Chemistry ,03 medical and health sciences ,0302 clinical medicine ,pneumatic tactile stimulation ,General Materials Science ,Instrumentation ,lcsh:QH301-705.5 ,Analysis method ,030304 developmental biology ,sensorimotor ,stroke rehabilitation ,Fluid Flow and Transfer Processes ,Physics ,0303 health sciences ,neurorehabilitation ,lcsh:T ,Process Chemistry and Technology ,General Engineering ,lcsh:QC1-999 ,motor ,Computer Science Applications ,lcsh:Biology (General) ,lcsh:QD1-999 ,Cerebral hemodynamics ,lcsh:TA1-2040 ,Functional near-infrared spectroscopy ,neuroprotection ,lcsh:Engineering (General). Civil engineering (General) ,hemodynamic response ,Neuroscience ,030217 neurology & neurosurgery ,lcsh:Physics - Abstract
Functional near-infrared spectroscopy (fNIRS) is an emerging technique in studying cerebral hemodynamics, however, consensus on the analysis methods and the clinical applications has yet to be established. In this study, we demonstrate the results of a pilot fNIRS study of cerebral hemodynamic response (HR) evoked by pneumotactile and sensorimotor stimuli on the dominant hand. Our goal is to find the optimal stimulus parameters to maximally evoke HR in the primary somatosensory and motor cortices. We use a pulsatile pneumatic array of 14 tactile cells that were attached to the glabrous surface of the dominant hand, with a patterned stimulus that resembles saltation at three distinct traverse velocities [10, 25, and 45 cm/s]. NIRS optodes (16 sources, 20 detectors) are bilaterally and symmetrically placed over the pre-and post-central gyri (M1 and S1). Our objective is to identify the extent to which cerebral HR can encode the velocity of the somatosensory and/or motor stimuli. We use common spatial pattern for feature extraction and regularized-discriminant analysis for classifying the fNIRS time series into velocity classes. The classification results demonstrate discriminatory features of the fNIRS signal from each distinct stimulus velocity. The results are inconclusive regarding the velocity which evokes the highest intensity of hemodynamic response.
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- 2020
- Full Text
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12. Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy.
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Feret, J. and Asner, G. P.
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REMOTE sensing , *AERIAL surveillance , *DISCRIMINANT analysis , *MULTIVARIATE analysis , *CONTRAST analysis (Mathematical statistics) - Abstract
We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy. [ABSTRACT FROM AUTHOR]
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- 2013
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13. The Tangent Classifier.
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Berrendero, JoséR. and Cárcamo, Javier
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SAMPLE size (Statistics) ,DISCRIMINANT analysis ,COVARIANCE matrices ,HETEROSCEDASTICITY ,APPROXIMATION theory ,HYPERPLANES - Abstract
Given a classifier, we describe a general method to construct a simple linear classification rule. This rule, called the tangent classifier, is obtained by computing the tangent hyperplane to the separation boundary of the groups (generated by the initial classifier) at a certain point. When applied to a quadratic region, the tangent classifier has a neat closed-form expression. We discuss various examples and the application of this new linear classifier in two situations under which standard rules may fail: when there is a fraction of outliers in the training sample and when the dimension of the data is large in comparison with the sample size. [ABSTRACT FROM AUTHOR]
- Published
- 2012
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14. Differentiation of two main ammunition brands in Chile by Regularized Discriminant Analysis (RDA) of metals in gunshot residues
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Yañez, Jorge, Paz Farías, María, Zúñiga, Valeria, Soto, César, Contreras, David, Pereira, Eduardo, Mansilla, Héctor D., Saavedra, Renato, Castillo, Rosario, and Sáez, Pedro
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AMMUNITION , *DISCRIMINANT analysis , *BULLET design & construction , *CHEMOMETRICS , *INDUCTIVELY coupled plasma atomic emission spectrometry - Abstract
Abstract: Conventionally, chemical patterns of gunshot residues (GSR) can be used for identification of a suspect involved in criminal fire arm incidents. Furthermore, metals composition in GSR is well related with the ammunition brand. In Chile the two main ammunition brands used are FAMAE and CBC. Metals, such as Pb, Ba, Sb, Cu, Zn and Ca are common elements detected in both brands. This work describes the application of both conventional and chemometric analysis of data (Regularized Discriminant Analysis, RDA) for differentiation of ammunition brands according to the metal patterns of GSR collected from shooter individuals. Real samples of GSR were collected from hands (dorsal region) of both shooters and non-shooters. Metals were analyzed using the techniques Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES). By means of conventional plotting techniques for differentiation, such as binary and ternary plots, differences between the two brands are observed although without quantitative certainty. For the first time, applying chemometric analysis, such as regularized discriminant analysis (RDA), the investigated ammunition brands can be classified and differentiated correctly with 100% certainty. [Copyright &y& Elsevier]
- Published
- 2012
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15. Weighted feature extraction with a functional data extension
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Giraldo, Luis Gonzalo Sánchez and Domínguez, Germán Castellanos
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GROUP extensions (Mathematics) , *DIMENSION reduction (Statistics) , *MATHEMATICAL proofs , *DISCRIMINANT analysis , *PRINCIPAL components analysis , *FEATURE extraction , *MACHINE learning - Abstract
Abstract: Dimensionality reduction has proved to be a beneficial tool in learning problems. Two of the main advantages provided by dimensionality reduction are interpretation and generalization. Typically, dimensionality reduction is addressed in two separate ways: variable selection and feature extraction. However, in the recent years there has been a growing interest in developing combined schemes such as feature extraction with built-in feature selection. In this paper, we look at dimensionality reduction as a rank-deficient problem that embraces variable selection and feature extraction, simultaneously. From our analysis, we derive a weighting algorithm that is able to select and linearly transform variables by fixing the dimensionality of the space where a relevance criterion is evaluated. This step enforces sparseness on the resulting weights. Our main goal is dimensionality reduction for classification problems. Namely, we introduce modified versions of principal component analysis (PCA) by expectation maximization (EM) and linear regularized discriminant analysis (RDA). Finally, we propose a simple extension of WRDA that deals with functional features. In this case, observations are described by a set of functions defined over the same domain. Methods were put to test on artificial and real data sets showing high levels of generalization even for small sized training samples. [Copyright &y& Elsevier]
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- 2010
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16. Discriminant analysis via support vectors
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Gu, Suicheng, Tan, Ying, and He, Xingui
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DISCRIMINANT analysis , *SUPPORT vector machines , *DIMENSION reduction (Statistics) , *KERNEL functions , *NONLINEAR theories , *COMPUTATIONAL complexity , *MATHEMATICAL transformations - Abstract
Abstract: In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for k-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, discriminant analysis via support vectors (SVDA), is proposed. First, the SVM is employed to compute an optimal direction to discriminant each two classes. Then, the criteria of class separability is constructed. At last, the projection matrix is computed. The kernel mapping idea is used to derive the non-linear version, kernel discriminant via support vectors (SVKD). In SVDA, only support vectors are involved to compute the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition. [Copyright &y& Elsevier]
- Published
- 2010
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17. Relationship between osteology and aquatic locomotion in birds: determining modes of locomotion in extinct Ornithurae.
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HINIĆ-FRLOG, S. and MOTANI, R.
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BIRD behavior , *BONES , *WATER birds , *ANIMAL species , *EXTINCT birds - Abstract
The evolutionary history of aquatic invasion in birds would be incomplete without incorporation of extinct species. We show that aquatic affinities in fossil birds can be inferred by multivariate analysis of skeletal features and locomotion of 245 species of extant birds. Regularized discriminant analyses revealed that measurements of appendicular skeletons successfully separated diving birds from surface swimmers and flyers, while also discriminating among different underwater modes of swimming. The high accuracy of this method allows detection of skeletal characteristics that are indicative of aquatic locomotion and inference of such locomotion in bird species with insufficient behavioural information. Statistical predictions based on the analyses confirm qualitative assessments for both foot-propelled (Hesperornithiformes) and wing-propelled ( Copepteryx) underwater locomotion in fossil birds. This is the first quantitative inference of underwater modes of swimming in fossil birds, enabling future studies of locomotion in extinct birds and evolutionary transitions among locomotor modes in avian lineage. [ABSTRACT FROM AUTHOR]
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- 2010
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18. Classiffication via Minimum Incremental Coding Length.
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Wright, John, Ma, Yi, Yangyu Tao, Zhouchen Lin, and Heung-Yeung Shum
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DATA compression ,DISCRIMINANT analysis ,CODING theory ,GAUSSIAN distribution ,DATA transmission systems - Abstract
We present a simple new criterion for classification, based on principles from lossy data compression. The criterion assigns a test sample to the class that uses the minimum number of additional bits to code the test sample, subject to an allowable distortion. We demonstrate the asymptotic optimality of this criterion for Gaussian distributions and analyze its relationships to classical classifiers. The theoretical results clarify the connections between our approach and popular classifiers such as maximum a posteriori (MAP), regularized discriminant analysis (RDA), k-nearest neighbor (k-NN), and support vector machine (SVM), as well as unsupervised methods based on lossy coding. Our formulation induces several good effects on the resulting classifier. First, minimizing the lossy coding length induces a regularization effect which stabilizes the (implicit) density estimate in a small sample setting. Second, compression provides a uniform means of handling classes of varying dimension. The new criterion and its kernel and local versions perform competitively on synthetic examples, as well as on real imagery data such as handwritten digits and face images. On these problems, the performance of our simple classifier approaches the best reported results, without using domain-specific information. All MATLAB code and classification results are publicly available for peer evaluation at http://perception.csl.uiuc.edu/coding/home.htm. [ABSTRACT FROM AUTHOR]
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- 2009
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19. Regularized mixture discriminant analysis
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Halbe, Zohar and Aladjem, Mayer
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UNIVERSAL algebra , *STATISTICAL correlation , *MULTIVARIATE analysis , *STATISTICAL sampling , *PATTERN recognition systems - Abstract
Abstract: In this paper, we seek a Gaussian mixture model (GMM) of the class-conditional densities for plug-in Bayes classification. We propose a method for setting the number of the components and the covariance matrices of the class-conditional GMMs. It compromises between simplicity of the model selection based on the Bayesian information criterion (BIC) and the high accuracy of the model selection based on the cross-validation (CV) estimate of the correct classification rate. We apply an idea of Friedman [Friedman, J.H. 1989. Regularized discriminant analysis. J. Amer. Statist. Assoc., 84, 165–175] to shrink a predefined covariance matrix to a parameterization with substantially reduced degrees of freedom (reduced number of the adjustable parameters). Our method differs from the original Friedman’s method by the meaning of the shrinkage. We operate on matrices computed for a certain class while the Friedman’s method shrinks matrices from different classes. We compare our method with the conventional methods for setting the GMMs based on the BIC and CV. The experimental results show that our method has the potential to produce parameterizations of the covariance matrices of the GMMs which are better than the parameterizations used in other methods. We observed significant enlargement of the correct classification rates for our method with respect to the other methods which is more pronounced as the training sample size decreases. The latter implies that our method could be an attractive choice for applications based on a small number of training observations. [Copyright &y& Elsevier]
- Published
- 2007
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20. Chemometric classification of gunshot residues based on energy dispersive X-ray microanalysis and inductively coupled plasma analysis with mass-spectrometric detection
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Steffen, S., Otto, M., Niewoehner, L., Barth, M., Bro¿żek-Mucha, Z., Biegstraaten, J., and Horváth, R.
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CHEMOMETRICS , *X-ray spectroscopy , *MICROCHEMISTRY , *SPECTRUM analysis - Abstract
Abstract: A gunshot residue sample that was collected from an object or a suspected person is automatically searched for gunshot residue relevant particles. Particle data (such as size, morphology, position on the sample for manual relocation, etc.) as well as the corresponding X-ray spectra and images are stored. According to these data, particles are classified by the analysis-software into different groups: ‘gunshot residue characteristic’, ‘consistent with gunshot residue’ and environmental particles, respectively. Potential gunshot residue particles are manually checked and – if necessary – confirmed by the operating forensic scientist. As there are continuing developments on the ammunition market worldwide, it becomes more and more difficult to assign a detected particle to a particular ammunition brand. As well, the differentiation towards environmental particles similar to gunshot residue is getting more complex. To keep external conditions unchanged, gunshot residue particles were collected using a specially designed shooting device for the test shots revealing defined shooting distances between the weapon''s muzzle and the target. The data obtained as X-ray spectra of a number of particles (3000 per ammunition brand) were reduced by Fast Fourier Transformation and subjected to a chemometric evaluation by means of regularized discriminant analysis. In addition to the scanning electron microscopy in combination with energy dispersive X-ray microanalysis results, isotope ratio measurements based on inductively coupled plasma analysis with mass-spectrometric detection were carried out to provide a supplementary feature for an even lower risk of misclassification. [Copyright &y& Elsevier]
- Published
- 2007
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21. A Simple Regularization Procedure for Discriminant Analysis.
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Nocairi, H., Qannari, E. M., and Hanafi, M.
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DISCRIMINANT analysis , *MULTIVARIATE analysis , *STATISTICAL correlation , *MATHEMATICAL variables , *SIMULATION methods & models - Abstract
Linear and quadratic discriminant analysis are likely to lead to unstable models and poor predictions in the presence of quasicolinearity among variables or in the case of the small sample and high-dimensional setting. A simple regularization procedure is proposed to cope with this problem. It is based on the introduction of a tuning parameter that draws a line between linear or quadratic discriminant analysis that is based on Mahalanobis distance and discriminant analysis based on the identity matrix. The tuning parameter is customized to individual situations by minimizing the cross-validated misclassification risk. The efficiency of the method of analysis in comparison with existing procedures is demonstrated on the basis of a data set and a large simulation study. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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22. Forensic Analysis Modeling and Chemometric Investigation of Molecular Markers from Natural and Engineered Environmental Systems.
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Kassim, Tarek A. and Simoneit, Bernd R. T.
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LIPIDS , *CHEMOMETRICS , *ENVIRONMENTAL quality , *ENVIRONMENTAL protection , *MOLECULES , *ENVIRONMENTAL sciences - Abstract
Investigations of natural and engineered environmental systems require achieving a complete characterization and identification of contaminants of concern. However, the differentiation of lipid molecular markers (MMs) that originated from various sources is difficult when based simply on their chemical compositions. A comprehensive fractionation protocol of lipid MMs is thus needed for discriminating contamination sources using chemometric (i.e., mathematical and statistical) techniques. The lack of information about the environmental quality of the coastal environment of Alexandria (Egypt) and the impact of anthropogenic sources of pollution in the area have initiated research to study the different extractable lipid classes and MMs in both natural and engineered environmental systems. Samples representing the complexity of the analyzed Alexandria environment were analyzed qualitatively and quantitatively for their lipid classes. A chemometric approach for the interpretation of the lipid data is presented. This unique approach consists of analysis of variance (ANOVA), discriminant partial least squares (D-PLS), and regularized discriminant analysis (RDA). ANOVA was used to compare the relative magnitudes of sample site and type variances. A variable selection approach based on the PLS regression coefficients was proposed to identify the most important lipid classes with their MMs for the classification, and to improve the results. RDA used a regularized covariance matrix estimate for the conventional statistical discriminant analysis methods. The results in this analysis indicated that: (a) the D-PLS and RDA methods provide satisfactory classification of lipid classes, with RDA being slightly better, and (b) the variable selection strategy was able to improve the classification results, and to help identify the most important lipid classes and contaminant MMs for distinguishing and characterizing the environmental samples. [ABSTRACT FROM AUTHOR]
- Published
- 2005
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23. Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition.
- Author
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Wen-Sheng Chen, Yuen, Pong C., Jian Huang, and Dao-Qing Dai
- Subjects
- *
FACIAL expression , *DISCRIMINANT analysis , *STATISTICAL correlation , *NONLINEAR statistical models , *STATISTICAL hypothesis testing , *KERNEL functions - Abstract
This paper addresses two problems in linear discriminant analysis (LDA) of face recognition. The first one is the problem of recognition of human faces under pose and illumination variations. It is well known that the distribution of face images with different pose, illumination, and face expression is complex and nonlinear. The traditional linear methods, such as LDA, will not give a satisfactory performance. The second problem is the small sample size (S3) problem. This problem occurs when the number of training samples is smaller than the dimensionality of feature vector. In turn, the within-class scatter matrix will become singular. To overcome these limitations, this paper proposes a new kernel machine-based one-parameter regularized Fisher discriminant (K1PRFD) technique. K1PRFD is developed based on our previously developed one-parameter regularized discriminant analysis method and the well-known kernel approach. Therefore, K1PRFD consists of two parameters, namely the regularization parameter and kernel parameter. This paper further proposes a new method to determine the optimal kernel parameter in RBF kernel and regularized parameter in within-class scatter matrix simultaneously based on the conjugate gradient method. Three databases, namely FERET, Yale Group B, and CMU PIE, are selected for evaluation. The results are encouraging. Comparing with the existing LDA-based methods, the proposed method gives superior results. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
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24. Results in statistical discriminant analysis: a review of the former Soviet Union literature
- Author
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Raudys, Šarūnas and Young, Dean M.
- Subjects
- *
PATTERN perception , *STATISTICS - Abstract
Much work in discriminant analysis and statistical pattern recognition has been performed in the former Soviet Union. However, most results derived by former Soviet Union researchers are unknown to statisticians and statistical pattern recognition researchers in the West. We attempt to give a succinct overview of important contributions by Soviet Block researchers to several topics in the discriminant analysis literature concerning the small training-sample size problem. We also include a partial review of corresponding work done in the West. [Copyright &y& Elsevier]
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- 2004
- Full Text
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25. Decodificación de la actividad cerebral mediante regularización con penalizantes mixtos
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Peterson, Victoria, Spies, Rubén Daniel, Tomassi, Diego, Biurrun Manresa, José, Diez, Pablo, Muravchik, Carlos, Rufiner, Hugo Leonardo, and Spies, Ruben Daniel
- Subjects
Regularized discriminant analysis ,Discriminative information ,purl.org/becyt/ford/1.2 [https] ,Penalización mixta ,Evoked potentials ,Ciencias de la Computación ,purl.org/becyt/ford/1 [https] ,Información discriminativa a-priori ,Imaginería motora ,Motor imagery ,Análisis discriminante regularizado ,Potenciales evocados ,Ciencias de la Computación e Información ,Interfaces cerebro-computadora ,Brain-computer interfaces ,Mixed penalization ,CIENCIAS NATURALES Y EXACTAS - Abstract
Fil: Peterson, Victoria. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. A brain computer interface (BCI) is a system which provides an alternative way of communication between the mind of a person and the outside world. An efficient and non-invasive way of establishing the communication is based on electroencephalography (EEG) and event-related potentials (ERPs). An ERP is an endogenous potential which results as a consequence of an external and relevant stimulus. For rehabilitation purposes most of the BCIs are based on motor imagery (MI), i.e. the mental simulation of movements. For the pattern recognition point of view both paradigms represent an extremely hard and challenging binary classification problem. The linear discriminant analysis (LDA) is a well-known and widely used dimensionality reduction tool in the context of supervised classification. Although LDA generally results in good classification performances while keeping the solution simple, it fails when the number of samples is large relative to the number of observations. Several authors have proposed different regularized versions of LDA, showing always the advantages of such tools. In this thesis we present the generalized sparse discriminant analysis (GSDA) method. This method automatically performs discriminative feature selection and classification by taking into account a-piori class discrepancy information. The GSDA method is designed to automatically select the optimal regularization parameters. Numerical experiments with both ERP-EEG and MI-EEG datasets are presented, showing that overall GSDA performance outperforms most state-of-the-art ERP and MI classification algorithms, for single-trial EEG classification. In addition, a feasibility study of a proposed method based on GSDA for MI detection in real-time scenarios is presented. Una interfaz cerebro-computadora (ICC) es un sistema que provee una alternativa forma de comunicación entre el cerebro de una persona y el mundo exterior. Una manera eficiente y no invasiva de medir la actividad cerebral es mediante electroencefalografía (EEG) de superficie. Si el objetivo es deletrear palabras, suelen utilizarse ICCs basadas en los potenciales relacionados a eventos (PREs). Para fines de rehabilitación, la mayoría de las BCIs se basan en el paradigma de imaginería motora (IM). En ambos paradigmas, la detección de la intención del usuario, puede tratarse como un problema de reconocimiento de patrones binario. El análisis discriminante lineal (LDA) es un método de clasificación muy conocido en el contexto de aprendizaje supervisado. Si bien LDA generalmente resulta en buenos desempeños de clasificación manteniendo la solución sencilla, el método falla cuando el número de muestras es relativamente grande en relación a la cantidad de observaciones. Varios autores han propuesto diferentes versiones regularizadas de LDA, mostrando siempre las ventajas del uso de tales técnicas. En esta tesis se ha desarrollado una versión penalizada y regularizada de LDA, denominada discriminante ralo generalizado (GSDA). Este método realiza selección de características junto con clasificación, considerando información discriminativa a-priori. Los experimentos numéricos muestran que la utilización de GSDA supera a los métodos del estado del arte para clasificación tanto de PREs como de IM. Asimismo, se presenta un estudio de la factibilidad de un método basado en GSDA para la detección de la intención del movimiento en tiempo real. Consejo Nacional de Investigaciones Científicas y Técnicas
- Published
- 2018
26. Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
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Esa Ollila and Muhammad Naveed Tabassum
- Subjects
FOS: Computer and information sciences ,Clustering high-dimensional data ,Machine Learning (stat.ML) ,Feature selection ,02 engineering and technology ,Statistics - Applications ,Regularization (mathematics) ,Methodology (stat.ME) ,Statistics - Machine Learning ,0202 electrical engineering, electronic engineering, information engineering ,Applications (stat.AP) ,Statistics - Methodology ,Sparse matrix ,ta113 ,business.industry ,Regularized discriminant analysis ,Pattern recognition ,Classification ,Linear discriminant analysis ,Thresholding ,ComputingMethodologies_PATTERNRECOGNITION ,Feature (computer vision) ,Sample size determination ,Gene expression microarrays ,020201 artificial intelligence & image processing ,Artificial intelligence ,Joint-sparse recovery ,business - Abstract
We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination. A regularization of the sample covariance matrix is also needed as we consider the challenging scenario where the number of features (variables) is comparable or exceeding the sample size of the training dataset. A simulation study and four examples of real-life microarray datasets evaluate the performances of CRDA based classifiers. Overall, the proposed method gives fewer misclassification errors than its competitors, while at the same time achieving accurate feature selection., Comment: 5 pages, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing 15-20 April 2018 | Calgary, Alberta, Canada
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- 2018
27. High-dimensional asymptotics of prediction: Ridge regression and classification
- Author
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Stefan Wager and Edgar Dobriban
- Subjects
Statistics and Probability ,FOS: Computer and information sciences ,Inverse ,Mathematics - Statistics Theory ,Machine Learning (stat.ML) ,02 engineering and technology ,Statistics Theory (math.ST) ,random matrix theory ,01 natural sciences ,regularized discriminant analysis ,010104 statistics & probability ,Matrix (mathematics) ,Statistics - Machine Learning ,62J05 ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,ridge regression ,Applied mathematics ,0101 mathematics ,Mathematics ,prediction error ,Covariance matrix ,Linear model ,020206 networking & telecommunications ,Covariance ,Linear discriminant analysis ,High-dimensional asymptotics ,Statistics, Probability and Uncertainty ,Operator norm ,Random matrix ,62H99 ,62H30 - Abstract
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where $p, n \to \infty$ and $p/n \to \gamma \in (0, \, \infty)$, and allow for arbitrary covariance among the features. For both methods, we provide an explicit and efficiently computable expression for the limiting predictive risk, which depends only on the spectrum of the feature-covariance matrix, the signal strength, and the aspect ratio $\gamma$. Especially in the case of regularized discriminant analysis, we find that predictive accuracy has a nuanced dependence on the eigenvalue distribution of the covariance matrix, suggesting that analyses based on the operator norm of the covariance matrix may not be sharp. Our results also uncover several qualitative insights about both methods: for example, with ridge regression, there is an exact inverse relation between the limiting predictive risk and the limiting estimation risk given a fixed signal strength. Our analysis builds on recent advances in random matrix theory., Comment: Added a section on prediction versus estimation for ridge regression. Rewrote introduction. Other results unchanged
- Published
- 2018
28. Regularization of the location model in discrimination with mixed discrete and continuous variables
- Author
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Merbouha, A. and Mkhadri, A.
- Subjects
- *
DISCRIMINANT analysis , *MULTIVARIATE analysis , *STATISTICAL correlation , *BAYESIAN analysis - Abstract
Regularized techniques in discriminant analysis with mixed discrete and continuous variables for generalized location model (GLOM) are presented. Three extensions are considered: constraining models, combining standard techniques and flexible Bayesian methods. The first approach is based on the flexibility in modelling the relationship among cell covariance matrices while at the same time keeping the number of unknown parameters reasonably small. The second approach is a regularized cell covariance matrices which takes a compromise between standard linear methods using two regularized parameters. The third approach develops a range of flexible Bayesian methods based on a conjugate and hierarchical covariance prior distributions akin to regularized GLOM. To assess the efficiency of these regularized versions, three real data sets are used for illustrations. The proposed methods compare very favourably with the classical GLOM. [Copyright &y& Elsevier]
- Published
- 2004
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29. Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition.
- Author
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Krzeszowski, Tomasz and Wiktorowicz, Krzysztof
- Subjects
- *
SWARM intelligence , *PARTICLE swarm optimization , *BIOMETRIC identification , *MATHEMATICAL optimization - Abstract
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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30. EEG intentions recognition in dynamic complex object control task by functional brain networks and regularized discriminant analysis.
- Author
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Fu, Rongrong, Wang, Han, Bao, Tiantian, and Han, Mengmeng
- Subjects
DISCRIMINANT analysis ,BRAIN waves ,BIOMEDICAL signal processing ,ELECTROENCEPHALOGRAPHY ,OBJECT manipulation ,INTENTION - Abstract
• A new EEG evoked paradigm combining physical interface and virtual feedback under the dynamic coupling system operation task is proposed, the experimental paradigm can better simulate real-life dynamic scenes, and EEG signals can be better expressed. • By constructing the brain wave of complex tasks by calculating the phase fluctuations of the EEG, this can more intuitively understand the relationship between the potential neuron mechanism and brain consciousness when performing tasks. • Decoding the network features by parameter-optimized regularized discriminant analysis, a good decoding effect is obtained by this method. Most of the tasks involved in neurological rehabilitation are generally constrained. However the complexity of experimental paradigms with constraints for acquiring electroencephalography (EEG) are relatively low. In order to improve the level of arousal in the brain, we propose an energy-constrained dynamic and complex experimental paradigm. In this novel experimental paradigm, EEG signals collected under the novel paradigm have different motor intentions. In this study, we combine the phase synchronization method and common spatial pattern (CSP) to build functional brain networks of subjects. Based on this method, we investigate the correlation of the global and local features of the functional brain networks and use regularized discriminant analysis (RDA) to recognize features. The accuracy obtained by the proposed method for EEG intention recognition can reach 93.47% (p < 0.01). The results show that the proposed method can decode EEG intention with high recognition performance during the manipulation of complex objects, which lays a foundation for the study of neurological rehabilitation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
31. Results in statistical discriminant analysis: a review of the former Soviet Union literature
- Author
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Dean M. Young and Šarūnas Raudys
- Subjects
Nonparametric error rate estimation ,Statistics and Probability ,Numerical Analysis ,Multivariate analysis ,Asymptotic error-rate approximations ,Nonparametric statistical classifiers ,Statistical pattern ,Regularized discriminant analysis ,Feature subset selection ,Linear discriminant analysis ,Plug-in statistical classifiers ,Econometrics ,Statistics, Probability and Uncertainty ,Soviet union ,Mathematics - Abstract
Much work in discriminant analysis and statistical pattern recognition has been performed in the former Soviet Union. However, most results derived by former Soviet Union researchers are unknown to statisticians and statistical pattern recognition researchers in the West. We attempt to give a succinct overview of important contributions by Soviet Block researchers to several topics in the discriminant analysis literature concerning the small training-sample size problem. We also include a partial review of corresponding work done in the West.
- Published
- 2004
32. An Empirical Analysis of Predictive Machine Learning Algorithms on High-Dimensional Microarray Cancer Data
- Author
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Bill, Jo A
- Subjects
- Data mining, Ensemble techniques, Machine learning, Predictive analytics, Regularized discriminant analysis, Support vector machines
- Abstract
This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space p is much larger than the number of observations n. Seven gene-expression microarray cancer datasets, where the ratio κ = n/p is less than one, were chosen for evaluation. The statistical and computational challenges inherent with this type of high-dimensional low sample size (HDLSS) data were explored. The capability and performance of a diverse set of machine learning algorithms is presented and compared. The sparsity and collinearity of the data being employed, in conjunction with the complexity of the algorithms studied, demanded rigorous and careful tuning of the hyperparameters and regularization parameters. This necessitated several extensions of cross-validation to be investigated, with the purpose of culminating in the best predictive performance. For the techniques evaluated in this thesis, regularization or kernelization, and often both, produced lower classification error rates than randomized ensemble for all datasets used in this research. However, no one technique evaluated for classifying HDLSS microarray cancer data emerged as the universally best technique for predicting the generalization error.1 From the empirical analysis performed in this thesis, the following fundamentals emerged as being instrumental in consistently resulting in lower error rates when estimating the generalization error in this HDLSS microarray cancer data: • Thoroughly investigate and understand the data • Stratify during all sampling due to the uneven classes and extreme sparsity of this data. • Perform 3 to 5 replicates of stratified cross-validation, implementing an adaptive K-fold, to determine the optimal tuning parameters. • To estimate the generalization error in HDLSS data, replication is paramount. Replicate R=500 or R=1000 times with training and test sets of 2/3 and 1/3, respectively, to get the best generalization error estimate. • Whenever possible, obtain an independent validation dataset. • Seed the data for a fair and unbiased comparison among techniques. • Define a methodology or standard set of process protocols to apply to machine learning research. This would prove very beneficial in ensuring reproducibility and would enable better comparisons among techniques. _____ 1A predominant portion of this research was published in the Serdica Journal of Computing (Volume 8, Number 2, 2014) as proceedings from the 2014 Flint International Statistical Conference at Kettering University, Michigan, USA.
- Published
- 2015
33. HIGH-DIMENSIONAL ASYMPTOTICS OF PREDICTION : RIDGE REGRESSION AND CLASSIFICATION
- Author
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Dobriban, Edgar and Wager, Stefan
- Published
- 2018
34. Functional Linear Discriminant Analysis for Irregularly Sampled Curves
- Author
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James, Gareth M. and Hastie, Trevor J.
- Published
- 2001
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