Feature structure fusion modelling for classification

Abstract

Structure fusion (SF) has been presented for multiple feature fusion via mining the discriminative and complementary information from different feature sets. As the typical methods, SF based on locality preserving projections (SFLPP) and SF based on tensor subspace analysis (SFTSA) have been developed for classification by capturing the complete structure from different features. However, the jointed optimisation function of SFLPP or SFTSA does not clearly explain the modelling mechanism of SF, and its solving process is complex because of iterative eigenvalue decomposition. In this study, structure modelling based on maximisation posterior probability (SMMPP) is proposed for solving these issues. It jointly considers both the certain prior structure (the mutual structure of multiple feature structure described by Ising model) and the uncertain likelihood structure (the possible fusion structure of multiple feature structure represented by Markov random field model) into the framework of Bayes' rule. The proposed computational solution is faster-converging speed than SFLPP or SFTSA with the guarantee of convergence. Extensive experiments conducted on shape analysis and human action recognition demonstrate the superiority of SMMPP over the state of art methods.