The symmetric eigenvalue problem
The symmetric eigenvalue problem
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Low Rank Approximations of Matrices
Machine Learning
Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
The theoretical analysis of GLRAM and its applications
Pattern Recognition
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
Preconditioned Lanczos method for generalized Toeplitz eigenvalue problems
Journal of Computational and Applied Mathematics
Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction
IEEE Transactions on Knowledge and Data Engineering
Incremental learning of bidirectional principal components for face recognition
Pattern Recognition
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
MPEG video watermarking using tensor singular value decomposition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A probabilistic model for image representation via multiple patterns
Pattern Recognition
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Matrix-based methods such as generalized low rank approximations of matrices (GLRAM) have gained wide attention from researchers in pattern recognition and machine learning communities. In this paper, a novel concept of bilinear Lanczos components (BLC) is introduced to approximate the projection vectors obtained from eigen-based methods without explicit computing eigenvectors of the matrix. This new method sequentially reduces the reconstruction error for a Frobenius-norm based optimization criterion, and the resulting approximation performance is thus improved during successive iterations. In addition, a theoretical clue for selecting suitable dimensionality parameters without losing classification information is presented in this paper. The BLC approach realizes dimensionality reduction and feature extraction by using a small number of Lanczos components. Extensive experiments on face recognition and image classification are conducted to evaluate the efficiency and effectiveness of the proposed algorithm. Results show that the new approach is competitive with the state-of-the-art methods, while it has a much lower training cost.