MSAV: An Unified Framework for Multi-view Subspace Analysis with View Consistence

With the development of multimedia period, information is always caputred with multiple views, which causes a research upsurge on multi-view learning. It is obvious that multi-view data contains more information than those single view ones. Therefore, it is crucial to develop the multi-view algorithms to adapt the demand of many applications. Even though some excellent multi-view algorithms were proposed, most of them can only deal with the specific problems. To tacle this problem, this paper proposes an unified framework named Multi-view Subspace Analysis with View Consistence (MSAV), which provides an unified means to extend those single-view dimension reduciton algorithms into multi-view versions. MSAV first extends multi-view data into kernel space to avoid the problem caused by different dimensions of the data from multiple views. Then, we introduced a self-weighted learning strategy to automatically assign weights for all views according to their importance. Finally, in order to promote the consistence of all views, Hilbert-Schmidt Independence Criterion is adopted by MSAV. Furthermore, We conducted experiments on several benchmark datasets to verify the performance of MSAV.