A unified tensor framework for face recognition
Pattern Recognition
Boosting discriminant learners for gait recognition using MPCA features
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
A new implementation of common matrix approach using third-order tensors for face recognition
Expert Systems with Applications: An International Journal
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Matrix-variate and higher-order probabilistic projections
Data Mining and Knowledge Discovery
Optimal calculation of tensor learning approaches
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In this paper, a multilinear formulation of the popular Principal Component Analysis (PCA) is proposed, named as multilinear PCA (MPCA), where the input can be not only vectors, but also matrices or higher-order tensors. It is a natural extension of PCA and the analogous counterparts in MPCA to the eigenvalues and eigenvectors in PCA are defined. The proposed MPCA has wide range of applications as a higher-order generalization of PCA. As an example, MPCA is applied to the problem of gait recognition using a novel representation called EigenTensorGait. A gait sequence is divided into half gait cycles and each half cycle, represented as a 3rd-order tensor, is considered as one data sample. Experiments show that the proposed MPCA performs better than the baseline algorithm in human identification on the Gait Challenge data sets.