MPCA: Multilinear Principal Component Analysis of Tensor Objects

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2D/3D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its two-dimensional (2DPCA) variant. Finally, a tensor

• Type: Journal
• Year: 2008
• Where: TNN
• Authors: Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos
• Keywords: Multilinear Principal Component, Principal Component Analysis, Tensor, TNN 2008