SUBSPACE CLUSTERING VIA INDEPENDENT SUBSPACE ANALYSIS NETWORK

Previous work on image clustering focused on seeking a low dimensional structure from the high-dimensional image data by a shallow linear model, such as sparse subspace clustering (SSC) or low-rank representation (LRR). The recent advance of deep learning shows its superiority via handling data with nonlinear structure, i.e., sparse auto-encoder and independent subspace analysis(ISA), etc. However, most of this type of methods may ignore lots of useful information embedded in the original data. To this end, we propose a novel unsupervised learning algorithm via ISA incorporating the subspace structure within data. Specifically, we adopt the ISA to learn local translation invariant feature from data and integrate a prior subspace information into the output of the network simultaneously. This method performs an impressive powerful ability to learn the nature of data. By evaluating on public databases, CMU-PIE and ORL, the experimental results show that the proposed approach achieves better clustering results compared with the state-of-the-art ones.