Within-class subspace regularization for human activity recognition

Abstract

A subspace regularization approach is proposed for eigenfeatures extraction and regularization in human activity recognition. In this approach the within-class subspace is modelled using more eigenvalues from the reliable subspace to obtain a four parameter modelling scheme. This regularization is done in one piece, therefore avoiding undue complexity of modelling eigenspectrum differently. The whole space is used for performance evaluation because feature extraction and dimensionality reduction is done at later stage of the evaluation process. Results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition.