Multilinear Principal Component Analysis Research Articles

Facial descriptor for Kinect depth using inner–inter-normal components local binary patterns and tensor histograms

RGB-D data collected from Microsoft Kinect are an easily available media providing the additional depth information besides RGB, which has great potentials in improving the performance of the facial analysis applications. In this paper, we focus on facial descriptor extraction from depth channel. First, we design a facial shape descriptor based on local binary patterns encoding from surface normal. The novelty lies in that it considers local structures involved in both inner component and inter-components of surface normal. Second, we propose a tensor representation for histogram arrays embedded in multidimensional space and further use multilinear principal component analysis to obtain an optimal trade-off between efficacy and efficiency. The experiments conducted on two publicly available databases, named CurtinFaces and Eurecom, have demonstrated the promising results achieved by proposed schemes on identity and ethnicity classification.

Sparse tensor canonical correlation analysis for micro-expression recognition

A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) [1] code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information.

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.

Compressed Submanifold Multifactor Analysis.

Although widely used, Multilinear PCA (MPCA), one of the leading multilinear analysis methods, still suffers from four major drawbacks. First, it is very sensitive to outliers and noise. Second, it is unable to cope with missing values. Third, it is computationally expensive since MPCA deals with large multi-dimensional datasets. Finally, it is unable to maintain the local geometrical structures due to the averaging process. This paper proposes a novel approach named Compressed Submanifold Multifactor Analysis (CSMA) to solve the four problems mentioned above. Our approach can deal with the problem of missing values and outliers via SVD-L1. The Random Projection method is used to obtain the fast low-rank approximation of a given multifactor dataset. In addition, it is able to preserve the geometry of the original data. Our CSMA method can be used efficiently for multiple purposes, e.g. noise and outlier removal, estimation of missing values, biometric applications. We show that CSMA method can achieve good results and is very efficient in the inpainting problem as compared to [1], [2]. Our method also achieves higher face recognition rates compared to LRTC, SPMA, MPCA and some other methods, i.e. PCA, LDA and LPP, on three challenging face databases, i.e. CMU-MPIE, CMU-PIE and Extended YALE-B.

A Report on Multilinear PCA Plus GTDA to Deal With Face Image

Abstract Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. One of these popular unsupervised multilinear algorithms is Multilinear Principal Component Analysis (MPCA) while another of the supervised multilinear algorithm is Multilinear Discriminant Analysis (MDA). Then a MPCA+MDA method has been introduced to deal with the tensorial signal. However, due to the no convergence of MDA, it is difficult for MPCA+MDA to obtain a precise result. Hence, to overcome this limitation, a new MPCA plus General Tensor Discriminant Analysis (GTDA) solution with well convergence is presented for tensorial face images feature extraction in this paper. Several experiments are carried out to evaluate the performance of MPCA+GTDA on different databases and the results show that this method has the potential to achieve comparative effect as MPCA+MDA.

A Distribution Model with Pattern Structure in Formal Concept Analysis for Meteorological Data Minging

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. Currently, gait from a single viewpoint has been used to recognize the gender of a person. Considering the multiple cameras used in real environments, we investigate gender classification from human gait by using multi-view fusion, a relatively understudied problem. In this paper, we present a new approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multi-linear principal component analysis (MPCA) is employed to reduce dimensionality of the tensor objects which integrate all views. The proposed fusion scheme is tested on CASIA database and compared with other fusion methods. The experimental results show that MPCA based feature fusion is quite effective for multi-view gait based gender classification.

Eigenbrains and Eigentensorbrains: Parsimonious bases for EEG biometrics

The use of electroencephalography (EEG) for biometric recognition purposes has recently received an increased level of attention thanks to some of its appealing properties. Among them, it is worth mentioning the universality, the intrinsic liveness detection capability, the possibility to perform a continuous identification, and the robustness against spoofing attacks. In this paper we exhaustively analyze the recognition performance achievable when using a parsimonious representation, in the frequency domain, of EEG signals acquired in both eyes-closed (EC) and eyes-open (EO) resting conditions. Specifically, we evaluate the effectiveness of EEG templates obtained as projections onto subspaces defined through eigenbrains (EBs) or eigentensorbrains (ETBs), two bases for EEG signals here defined by means of principal component analysis (PCA) and multilinear PCA (MPCA). An extensive set of experimental tests, conducted on a database comprising EEG recordings acquired from 30 subjects during two separate sessions, in different days, is performed to compare the recognition capabilities of the considered representations under different system configurations.

Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

A Report on Uncorrelated Multilinear Principal Component Analysis Plus Extreme Learning Machine to Deal With Tensorial Data

A Report on Uncorrelated Multilinear Principal Component Analysis Plus Extreme Learning Machine to Deal With Tensorial Data

Uncorrelated Multilinear Principal Component Analysis Plus Linear Discriminant Analysis: Effective Feature Extraction for Structural Magnetic Resonance Imaging Data of Alzheimer Disease

Uncorrelated Multilinear Principal Component Analysis Plus Linear Discriminant Analysis: Effective Feature Extraction for Structural Magnetic Resonance Imaging Data of Alzheimer Disease

Face Recognition Based on Uncorrelated Multilinear PCA Plus Classical LDA

Subspace learning is an important direction in computer vision research. In this paper, a new method of face recognition based on uncorrelated multilinear principal component analysis (UMPCA) and linear discriminant analysis (LDA) is proposed. First, instead of transforming matrices into vectors for principal component analysis (PCA), UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. A subset of features is extracted and the classical LDA is then applied to find the best subspaces. Finally, the comprehensive experiments are provided on AT&T databases and the experiment results show its superiority through the comparison with other PCA plus LDA based algorithms.

A noise robust speech features extraction approach in multidimensional cortical representation using multilinear principal component analysis

In this paper, we propose a new type of noise robust feature extraction method based on multidimensional perceptual representation of speech in the auditory cortex (AI). Different coded features in different dimensions cause an increase in discrimination power of the system. On the other hand, this representation causes a great increase in the volume of information that produces the curse of dimensionality phenomenon. In this study, we propose a second level feature extraction stage to make the features suitable and noise robust for classification training. In the second level of feature extraction, we target two main concerns: dimensionality reduction and noise robustness using singular value decomposition (SVD) approach. A multilinear principal component analysis framework based on higher-order SVD is proposed to extract the final features in high-dimensional AI output space. The phoneme classification results on different subsets of the phonemes of additive noise contaminated TIMIT database confirmed that the proposed method not only increased the classification rate considerably, but also enhanced the robustness significantly comparing to conventional Mel-frequency cepstral coefficient and cepstral mean normalization features, which were used to train in the same classifier.

Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.

Multiple Morphological Profiles From Multicomponent-Base Images for Hyperspectral Image Classification

Morphological profiles (MPs) are a useful tool for remotely sensed image classification. These profiles are constructed on a base image that can be a single band of a multicomponent remote sensing image. Principal component analysis (PCA) has been used to provide other base images to construct MPs in high-dimensional remote sensing scenes such as hyperspectral images [e.g., by deriving the first principal components (PCs) and building the MPs on the first few components]. In this paper, we discuss several strategies for producing the base images for MPs, and further categorize the considered methods into four classes: linear, nonlinear, manifold learning-based, and multilinear transformation-based. It is found that the multilinear PCA (MPCA) is a powerful approach for base image extraction. That is because it is a tensor-based feature representation approach, which is able to simultaneously exploit the spectral-spatial correlation between neighboring pixels. We also show that independent component analysis (ICA) is more effective for constructing base images than PCA. Another important contribution of this paper is a new concept of multiple MPs (MMPs), aimed at synthesizing the spectral-spatial information extracted from the multicomponent base images, and further enhancing the classification accuracy of MPs. Moreover, we propose two different strategies to interpret the newly proposed MMPs by considering their hyperdimensional feature space: decision fusion and sparse classifier based on multinomial logistic regression (MLR). Experiments conducted on three well-known hyperspectral datasets are used to quantitatively assess the accuracy of different algorithms.

Multilinear sparse principal component analysis.

In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.

Speech Emotion Recognition with MPCA and Kernel Partial Least Squares Regression

Speech signal is one of the major means for communication which carries not only semantic, but personal information, such as genders and emotions. The researches about speech emotion have become more and more important to human-computer interaction. To this end, from speech, the long-term and short-term emotional features are extracted, the dimensionality of which is then reduced by virtue of the multi linear PCA algorithm. Finally, the kernel partial least squares regression is used for speech emotional recognition. The results show that in comparison with other current classifiers, the algorithm proposed herein can improve recognition rates by about 6% to 23%.

Monitoring of the World’s Environmental Risk Based on MPCA

MPCA (multilinear principal component analysis) and hypothesis testing are used to establish the environmental monitoring system, including both the regional and the variable factors. High-dimensional data make it advantage than the previous models in considering comprehensive high-dimensional variables. So simulation of the system measures the change and change range of the environment, providing a more precise reference about the status of the environment.

An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while theKnearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA) based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA), incremental principal component analysis (IPCA), and multilinear principal component analysis (MPCA) algorithms. At the same time, ITPCA also has lower time and space complexity.

Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications

The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and theℓ1-norm. The problem is well structured in both the objective function and constraints. We apply augmented Lagrange multiplier method which can make use of the good structure for efficiently solving this problem. In the experiments, the algorithm is compared with the state-of-art algorithm both on synthetic data and real data including traffic data, image data, and video data.