Improved eigenspectrum regularisation for human activity recognition

Abstract

A within-class subspace regularisation approach is proposed for eigenfeatures extraction and regularisation 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 model enables a better and true estimation of the eigenvalues that are distorted by the small sample size effect. This regularisation is done in one piece, thereby avoiding undue complexity of modelling eigenspectrum differently. The whole eigenspace is used for performance evaluation because feature extraction and dimensionality reduction are done at a later stage of the evaluation process. The results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition.