Semi-supervised Symmetric Matrix Factorization with Low-Rank Tensor Representation

arXiv:2405.02688v1 Announce Type: new
Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., they either directly refine the similarity matrix element-wisely or restrain the distance of the decomposed vectors in pairs according to the pairwise constraints, which overlook the global perspective, i.e., in the ideal case, the pairwise constraint matrix and …

arxiv cs.lg factorization low matrix representation semi semi-supervised tensor type