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Since the transmission path of inter-shaft bearing-fault signal is complex, a fault feature extraction method based on hierarchical permutation entropy (HPE) and locally linear embedding (LLE) algorithm is proposed in this paper. In this method, HPE is utilized to extract fault information of signals, and LLE is utilized to reduce and fuse high-dimensional fault features of multi-sensors to construct fault samples. Then, the random forest (RF) model is established to diagnose the faults of the inter-shaft bearings. The fault simulation test rig with the inter-shaft bearing is built to simulate the normal bearing, inner ring fault, outer ring fault, and rolling ball fault, and the data are collected to verify the HPE-LLE-RF fault diagnosis algorithm of inter-shaft bearings established in this paper. The experimental results show that the proposed algorithm can extract the fault features of inter-shaft bearings effectively with a fault diagnosis accuracy of 93.3% without overfit phenomenon. [ABSTRACT FROM AUTHOR]

This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.

This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection. [ABSTRACT FROM AUTHOR]

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Natural gas pipeline leaks can cause serious hazards to natural gas transportation and pose considerable risks to the environment and the safety of residents. Therefore, feature extraction of pipeline signals is crucial in natural gas pipeline leak detection. However, the quality of feature extraction directly affects the effectiveness of pipeline leak detection. Therefore, this paper proposes a pipeline leakage feature extraction method based on variable mode decomposition (VMD) and local linear embedding (LLE). First, the pipeline signal is decomposed into several modal components by VMD; then, the dispersion entropy is used to select the feature modes. Secondly, the time-frequency domain features of different components are extracted to construct a high-dimensional feature matrix, which LLE reduces to obtain the classified low-dimensional feature vectors. Finally, the extracted feature vectors are used to train and test the support vector machine (SVM). By analyzing the experimental results, it can be seen that the proposed method can classify pipeline signals with an accuracy of up to 95%, which effectively solves the problem of false alarms and missed alarms in pipeline leakage detection. [ABSTRACT FROM AUTHOR]

The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings. [ABSTRACT FROM AUTHOR]

Locally linear embedding (LLE) is an effective tool to extract the significant features from a dataset. However, most of the relevant existing algorithms assume that the original dataset resides on a Euclidean space, unfortunately nearly all the original data space is non-Euclidean. In addition, the original LLE does not use the discriminant information of the dataset, which will degrade its performance in feature extraction. To address these problems raised in the conventional LLE, we first employ the original dataset to construct a symmetric positive definite manifold, and then estimate the tangent space of this manifold. Furthermore, the local and global discriminant information are integrated into the LLE, and the improved LLE is operated in the tangent space to extract the important features. We introduce Iris dataset to analyze the capability of the proposed method to extract features. Finally, several experiments are performed on five machinery datasets, and experimental results indicate that our proposed method can extract the excellent low-dimensional representations of the original dataset. Compared with the state-of-the-art methods, the proposed algorithm shows a strong capability for fault diagnosis. [ABSTRACT FROM AUTHOR]

In the era of healthcare, and its related research fields, the dimensionality problem of high dimensional data is a massive challenge as it contains a huge number of variables forming complex data matrices. The demand for dimension reduction of complex data is growing immensely to improvise data prediction, analysis and visualization. In general, dimension reduction techniques are defined as a compression of dataset from higher dimensional matrix to lower dimensional matrix. Several computational techniques have been implemented for data dimension reduction, which is further segregated into two categories such as feature extraction and feature selection. In this review, a detailed investigation of various feature extraction and feature selection methods has been carried out with a systematic comparison of several dimension reduction techniques for the analysis of high dimensional data and to overcome the problem of data loss. Then, some case studies are also cited to verify the better approach for data dimension reduction by considering few advances described in the technical literature. This review paper may guide researchers to choose the most effective method for satisfactory analysis of high dimensional data. [ABSTRACT FROM AUTHOR]

The locally linear embedding aims to extract the significant features by only digging the individual geometric structure of original data set, for which the intrinsic features can not be completely expressed. In this study, two LLE-based multi-structure fusion methods are proposed. In the proposed methods, the least squares and sparse structures are first estimated in original space, and then the coefficient and function fusion approaches are introduced to integrate the least squares and sparse structures. Furthermore, the relationship between the two fusion methods are analyzed and we demonstrate that the solution of coefficient fusion is a subset of the one of the function fusion. Extensive experiments are carried out on benchmark fault data set and the bearing data set collected from our own laboratory, and experimental results indicate that the proposed multi-structure methods outperform the existing state-of-art related methods. [ABSTRACT FROM AUTHOR]

Hyperspectralimages (HSIs) can provide powerful spectral discriminative information for the land-covers, thus is widely used in classification and target detection. However, HSIs always suffer from the curse of high dimensionality due the high spectral dimension, therefore dimension reduction and feature extraction are essential for the application of HSIs. In this paper, we propose an unsupervised feature extraction method for HSIs using combined low rank representation and locally linear embedding (LRR and LLE). LRR can structurally represent the intrinsic property of union of low-rank subspaces and LLE can employ the spatial correlation information. Two real HSI datasets are used in the experiments and the classification results using support vector machine (SVM) demonstrate that the features extracted by LRR LLE are more discriminative than the state-of-art methods. The classification accuracy of LRR LLE versus IR improved by an average of 4.47% and 2.97% on OA and AA, respectively; compared with the original data, it increased by approximately 12.07% and 7.35%. [ABSTRACT FROM AUTHOR]

Automated fault diagnosis of analogue circuits makes use of dimension reduction (DR) technologies to extract fault features. Traditional DR methods such as the principal component analysis (PCA) and kernel based DR methods have achieved valuable results. However, these methods give inadequate consideration to the geometric structure of fault data which is embedded in high dimensional spaces (HDS). This negatively affects the low-dimensional features. Recently introduced, manifold learning methods have the ability to recover the intrinsic geometric structure of a data set in low dimensional spaces (LDS). Locally linear embedding (LLE) and diffusion maps (DM) are two popular approaches of manifold learning methods. The former preserves the local linear structure of a data set and the latter is resistant to outlier effects. We present a novel DR algorithm using both LLE and DM to extract fault features based on the analysis of the intrinsic geometric structure of fault data. First, by the correlation analysis of the original fault data, we conclude that they are locally linear dependent, and then use LLE to obtain mid-level fault features. The calculation of local outlier factor (LOF) values confirms the existence of outliers in the fault data with mid-level features. Then, we apply DM to eliminate the negative effect of outliers and derive visual data with high-level features. Our experimental results show that the proposed algorithm offers performance superior to the traditional DR methods regardless of whether the circuit is linear or non-linear. [ABSTRACT FROM AUTHOR]

Human-activity recognition and seizure-detection techniques have gathered pace with the widespread availability of wearable devices. A study of the literature shows various studies for 3D accelerometer-based seizure detection that describe the selection of acceleration variables and controlled transformations, while discarding the remaining input variable contributions. The aim of this research is to evaluate feature extraction based on different techniques and with the advantage of an overview of all information on the problem. Three feature extraction techniques – namely, Locally Linear Embedding, Principal Component Analysis (PCA) and a Distance-Based PCA – are analyzed and their outcomes compared against K-Nearest Neighbor and Decision Trees. A realistic experimentation simulating epileptic mioclonic convulsions was performed. The PCA-based methods were found to produce solutions that managed the problem perfectly well, either learning specific models for each individual or learning generalized models. [ABSTRACT FROM AUTHOR]

• Linear approximation of LLE. Our model takes the linear mapping into consideration, making it more suitable to handle both linear and non-linear data. Also, outliers can be well dealt with during the linear projection procedure. • Global and local structure. Instead of only consider the local geometry properties, we also consider the global data point relationship to preserve the intrinsic structure. And our model shows robustness to noise and uneven distribution data. • Adaptive neighbor selection. In our model, weight between data and neighbors are updated in order to adjust each data into its optimal neighborhood. Using adaptive neighbor strategy, manifold structure can be kept and then structure learning and feature extraction could be accomplished simultaneously. Dimensionality reduction is one of the most important techniques in the field of data mining. It embeds high-dimensional data into a low-dimensional vector space while keeping the main information as much as possible. Locally Linear Embedding (LLE) as a typical manifold learning algorithm computes neighborhood preserving embeddings of high-dimensional inputs. Based on the thought of LLE, we propose a novel unsupervised dimensionality reduction model called Local Linear Embedding with Adaptive Neighbors (LLEAN). To achieve a desirable dimensionality reduction result, we impose adaptive neighbor strategy and adopt a projection matrix to project data into an optimal subspace. The relationship between every pair-wise data is investigated to help reveal the data structure. Augmented Lagrangian Multiplier (ALM) is devised in optimization procedure to effectively solve the proposed objective function. Comprehensive experiments on toy data and benchmark datasets have been done and the results show that LLEAN outperforms other state-of-the-art dimensionality reduction methods. [ABSTRACT FROM AUTHOR]

Supervised neighborhood preserving embedding (SNPE), a nonlinear dimensionality reduction method, was employed to represent near-infrared (NIR) and Raman spectral features of agricultural samples (Angelica gigas, sesame, and red pepper), and the newly constructed variables were used to discriminate their geographical origins. This study was done to evaluate the potential of SNPE for recognizing minute spectral differences between classes by preserving local relationships, in comparison with widely adopted linear feature representation methods such as principal component analysis (PCA) and partial least squares (PLS). For this purpose, diffuse reflectance NIR spectral datasets of Angelica gigas, sesame, and red pepper, and a Raman spectral dataset of the same red pepper were prepared. The spectra were represented into new variables in reduced dimensions by PCA, PLS, neighborhood preserving embedding (NPE), and SNPE, and the represented variables were used to determine the geographical origins of samples by using the k -nearest neighbor ( k -NN) and support vector machine (SVM). The combination of SNPE and SVM differentiated the geographical origins with improved accuracy. Overall results demonstrate that SNPE is a valuable alternative feature representation method, especially when complex and highly overlapping vibrational spectra are used for analysis. [ABSTRACT FROM AUTHOR]

Smart substation is a crucial Cyber-Physical system and is prone to cyber-attack. In this paper, we propose a novel anomaly detection mechanism tailored for detecting the IEC 61850-based network traffic. Three types of traffic features are taken into account for comprehensively characterizing the network traffic during a time window. To eliminate the subjectivity of manually selecting the traffic features, we exploit Discrete Wavelet Transform (DWT) algorithm to secondarily extract the deep features. An improved Locally Linear Embedding (LLE) algorithm is proposed to reduce the dimension of deep feature vectors with more effective dimensionality reduction ability. By doing so, the LSTM (Long Short Term Memory)-based Autoencoder network that can learn to reconstruct the normal traffic time-series behavior, and thereafter uses the reconstruction error to detect the anomalies. We assess the performance of our proposed mechanism with the comprehensive experiments on the real smart substation. The results indicate that the proposed mechanism can be performed in a fast way with satisfactory detection performance. [ABSTRACT FROM AUTHOR]

Abstract: At present, dictionary based models have been widely used in image classification. The image features are approximated as a linear combination of bases selected from the dictionary in a sparse space, resulting in compact patterns. The features applied to image classification usually reside on low dimensional manifolds embedded in a high dimensional ambient space; traditional sparse coding algorithm, however, does not consider this topological structure. It can be characterized naturally by linear coefficients that reconstruct each data point from its neighbors. One of the central issues here is how to determine the neighbors and learn the coefficients. In this paper, the geometrical structures are encoded in two situations. In simple cases when data points distribute on a single manifold, it is explicitly modeled by locally linear embedding algorithm combined with k-nearest neighbors. Nevertheless, in real-world scenarios, complex data points often lie on multiple manifolds. Sparse representation algorithm combined with k-nearest neighbors is instead utilized to construct the topological structures, because it is capable of approximating the data point by selecting its homogenous neighbors adaptively to guarantee the smoothness of each manifold. After obtaining the local fitting relationship, these two topological structures are then embedded into sparse coding algorithm as regularization terms to formulate the corresponding objective functions of dictionary learning on single manifold (DLSM) and dictionary learning on multiple manifolds (DLMM), respectively. Upon this, a coordinate descent scheme is proposed to solve the unified optimization problems. Experimental results on several benchmark data sets, such as Caltech-256, Caltech-101, Scene 15, and UIUC-Sports, show that our proposed algorithms equal or outperform other state-of-the-art image classification algorithms. [Copyright &y& Elsevier]

Locally linear embedding is often invalid for sparse data sets because locally linear embedding simply takes the reconstruction weights obtained from the data space as the weights of the embedding space. This paper proposes an improved method for sparse data sets, a united locally linear embedding, to make the reconstruction more robust to sparse data sets. In the proposed method, the neighborhood correlation matrix presenting the position information of the points constructed from the embedding space is added to the correlation matrix in the original space, thus the reconstruction weights can be adjusted. As the reconstruction weights adjusted gradually, the position information of sparse points can also be changed continually and the local geometry of the data manifolds in the embedding space can be well preserved. Experimental results on both synthetic and real-world data show that the proposed approach is very robust against sparse data sets. [ABSTRACT FROM AUTHOR]

Abstract: The low-dimensional representation of high-dimensional data and the concise description of its intrinsic structures are central problems in data analysis. In this paper, an unsupervised learning algorithm called weighted locally linear embedding (WLLE) is presented to discover the intrinsic structures of data, such as neighborhood relationships, global distributions and clustering. The WLLE algorithm is motivated by locally linear embedding (LLE) algorithm and cam weighted distance, a novel distance measure which usually gives a deflective cam contours for equal-distance contour in classification for an improved classification. It is a major advantage of the WLLE to optimize the process of intrinsic structure discovery by avoiding unreasonable neighbor searching, and at the same time, allow the discovery adapt to the characteristics of input data set. Furthermore, the algorithm discovers intrinsic structures which can be used to compute manipulative embedding for potential classification and recognition purposes, thus can work as a feature extraction algorithm. Simulation studies demonstrate that the WLLE can give better results in manifold learning and dimension reduction than LLE and neighborhood linear embedding (NLE), and is more robust to parameter changes. Experiments on face images data sets and comparison to other famous face recognition methods such as kernel-PCA (KPCA) and kernel direct discriminant analysis (KDDA) are done to show the potential of WLLE for real world problem. [Copyright &y& Elsevier]

Abstract: In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]