Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression ...

clustering matrix-factorization constrained-optimization data-analysis outlier-detection clustering-algorithm nmf resource-allocation nonnegativity-constraints anomaly-detection orthogonal nonnegative ...

This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are ...

The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University ...