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

May 11, 2022, 1:11 a.m. | Tri Nguyen, Xiao Fu, Ruiyuan Wu

cs.LG updates on arXiv.org arxiv.org

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

More from arxiv.org / cs.LG updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California