Abstract
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.
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead
I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.