An enhanced high-order Boltzmann machine for feature engineering

摘要

Recently, automatic feature extraction and selection from unlabeled images that contain irrelevant patterns have been a proceeding interest. In this paper, an enhanced high-order Boltzmann machine is designed to promote the capacity of feature extraction and selection in a unified context. First, gating mechanism is employed for feature selection in comparison with conventional approaches. Then, two sets of hidden variables that the one set is real-valued latent variables and the other is spike latent variables are introduced to model the covariance structure of local patches, which can boost the abilities of feature learning and feature selection in turn. Simultaneously, the proposed model can infer in parallel via easy block Gibbs sampling without much training difficulty. Last, several extensions of the proposed model are developed to cope with different scenes. The massive performances obtained from various visual tasks have demonstrated that the proposed model can reach the highly improved performances over several currently excellent methods.