Deep nonnegative matrix factorization (deep NMF) has recently emerged as a valuable
technique for extracting multiple layers of features across different scales. However, all …

In this paper, we propose a new low-rank matrix factorization model, dubbed bounded
simplex-structured matrix factorization (BSSMF). Given an input matrix X and a factorization …

In this article, we propose a new low-rank matrix factorization model dubbed bounded
simplex-structured matrix factorization (BSSMF). Given an input matrix and a factorization …

Nonnegative Matrix Factorization (NMF) is a versatile and powerful tool for discovering latent
structures in data matrices, with many variations proposed in the literature. Recently, Leplat …

We propose a Block Majorization Minimization method with Extrapolation (BMMe) for solving
a class of multi-convex optimization problems. The extrapolation parameters of BMMe are …

Abstract Deep Nonnegative Matrix Factorization (deep NMF) has recently emerged as a
valuable technique for extracting multiple layers of features across different scales …

Low-rank matrix approximation is a standard, yet powerful, embedding technique that can
be used to tackle a broad range of problems, including the recovery of missing data. In this …