Robust non-negative matrix factorization via joint sparse and graph regularization
In real world applications, we often have to deal with some high-dimensional, sparse and noisy data. In this paper, we aim to handle this kind of complex data by a Robust Non-negative Matrix Factorization via joint Sparse and Graph regularization model (RSGNMF). We provide a novel efficient and elegant iterative updating algorithm with rigorous convergence analysis for RSGNMF model. Experimental results on image data sets demonstrate that our RSGNMF model outperforms existing start-of-art methods.