Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Parallel Image Matrix Compression for Face Recognition
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Knowledge and Information Systems
Image Recognition Using Weighted Two-Dimensional Maximum Margin Criterion
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic Column-Based Matrix Decomposition
IEEE Transactions on Knowledge and Data Engineering
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Bayesian Tensor Approach for 3-D Face Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
I-vector based speaker recognition using advanced channel compensation techniques
Computer Speech and Language
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In this paper, we propose tensor based Maximum Margin Criterion algorithm (TMMC) for supervised dimensionality reduction. In TMMC, an image object is encoded as an nth-order tensor, and its 2-D representation is directly treated as matrix. Meanwhile, the k-mode optimization approach is exploited to iteratively learn multiple interrelated discriminative subspaces for dimensionality reduction of the higher order tensor. TMMC generalizes the traditional MMC based on vector data to the one based on matrix and tensor data, which completes the MMC family in terms of data representation. The results of experiments conducted on four databases show that the accurate recognition rate of TMMC is better than that of the method of Concurrent Subspaces Analysis (CSA), and is comparable with the method of Multilinear Discriminant Analysis (MDA). The experimental results also show that the accurate recognition rate of the tensor/matrix-based methods may not always be better than that of vector-based methods. Reasonable discussions about this phenomenon have been given in this paper.