Two-dimensional subspace classifiers for face recognition
Neurocomputing
Proximal support vector machine using local information
Neurocomputing
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Mode-kn factor analysis for image ensembles
IEEE Transactions on Image Processing
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Generalized discriminant analysis: a matrix exponential approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Maximum margin criterion with tensor representation
Neurocomputing
Boosting a multi-linear classifier with application to visual lip reading
Expert Systems with Applications: An International Journal
Newborn footprint recognition using subspace learning methods
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Plant classification using leaf image based on 2D linear discriminant analysis
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
Bimode model for face recognition and face representation
Neurocomputing
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Discriminant orthogonal rank-one tensor projections for face recognition
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
Multilinear nonparametric feature analysis
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Expert Systems with Applications: An International Journal
Separable linear discriminant analysis
Computational Statistics & Data Analysis
Higher rank Support Tensor Machines for visual recognition
Pattern Recognition
Tensor rank one differential graph preserving analysis for facial expression recognition
Image and Vision Computing
Modular discriminant analysis and its applications
Artificial Intelligence Review
Toward an efficient and scalable feature selection approach for internet traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Target detection based on a dynamic subspace
Pattern Recognition
Multiple rank multi-linear SVM for matrix data classification
Pattern Recognition
Multi-linear neighborhood preserving projection for face recognition
Pattern Recognition
Integration of multi-feature fusion and dictionary learning for face recognition
Image and Vision Computing
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There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes