Nonnegative matrix factorization with local similarity learning

Existing nonnegative matrix factorization methods usually focus on learning global structure of the data to construct basis and coefficient matrices, which ignores local that commonly exists among data. To overcome this drawback, in paper, we propose a new type method, learns similarity clustering mutually enhanced way. The learned representation is more representative it better reveals inherent geometric property Moreover, performed kernel space, enhances capability proposed model discovering nonlinear structures Multiplicative updating rules are developed with theoretical convergence guarantees. Extensive experimental results have confirmed effectiveness model.