STPCA: Sparse Tensor Principal Component Analysis for Feature Extraction

Due to the fact that many objects in the real world can be naturally represented as tensors, tensor subspace analysis has become a hot research area in pattern recognition and computer vision. However, existing tensor subspace analysis methods cannot provide an intuitionistic nor semantic interpretation for the projection matrices. In this paper, we propose Sparse Tensor Principal Component Analysis (STPCA), which transforms the eigen-decomposition problem to a series of regression problems. Since its projection matrices are sparse, STPCA can also address the occlusion problem. Experiment on Georgia tech database and AR database showed that the proposed method outperforms the Multilinear Principal Component Analysis (MPCA) in terms of accuracy and robustness.