Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
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We propose a multi-linear algebra based subspace learning approach for finding linear projection which preserves some implicit structural or locally-spatial information among the original feature space. Our method uses a new tensor data representation model, in which, each group of data points are partitioned into several equal-sized sub-groups with its neighbors affiliated to them, and all sub-groups are concatenated to represent as the tensor space product of the original feature space. Then, a new optimization algorithm called Lagrangian multiplier mode (L-mode) is presented for computing the optimal linear projections. We show that our method has three ways for resolving the Small Sample Size problem: by applying the fuzzy matrix model to avoid the disturbance from non-interested determinant, by a quadratic sample correlation model, and by projecting the samples into a manifold using linear programming. Extensive experimental results conducted on two benchmark face biometrics datasets i.e. Yale-B and CMU-PIE, and a nutrition surveillance dataset demonstrate that our method is effective and robust than the state-of-the-arts such as Principal Component Analysis, Linear Discriminant Analysis, Locality Preserving Projections and their variations on both classification accuracies and computational expenses.