Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach. (arXiv:2109.11135v2 [eess.SP] UPDATED)

Web: http://arxiv.org/abs/2109.11135

Nonnegative matrix factorization (NMF) often relies on the separability
condition for tractable algorithm design. Separability-based NMF is mainly
handled by two types of approaches, namely, greedy pursuit and convex
programming. A notable convex NMF formulation is the so-called self-dictionary
multiple measurement vectors (SD-MMV), which can work without knowing the
matrix rank a priori, and is arguably more resilient to error propagation
relative to greedy pursuit. However, convex SD-MMV renders a large memory cost
that scales quadratically with the problem size. …

arxiv dictionary factorization memory optimization