Web: http://arxiv.org/abs/2112.05664

Sept. 23, 2022, 1:12 a.m. | Sixin Zhang, Emmanuel Soubies, Cédric Févotte

cs.LG updates on arXiv.org arxiv.org

Non-negative matrix factorization with transform learning (TL-NMF) is a
recent idea that aims at learning data representations suited to NMF. In this
work, we relate TL-NMF to the classical matrix joint-diagonalization (JD)
problem. We show that, when the number of data realizations is sufficiently
large, TL-NMF can be replaced by a two-step approach -- termed as JD+NMF --
that estimates the transform through JD, prior to NMF computation. In contrast,
we found that when the number of data realizations is …

arxiv

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

Machine Learning Product Manager (Canada, Remote)

@ FreshBooks | Canada

Data Engineer

@ Amazon.com | Irvine, California, USA

Senior Autonomy Behavior II, Performance Assessment Engineer

@ Cruise LLC | San Francisco, CA

Senior Data Analytics Engineer

@ Intercom | Dublin, Ireland

Data Analyst Intern

@ ADDX | Singapore

Data Science Analyst - Consumer

@ Yelp | London, England, United Kingdom

Senior Data Analyst - Python+Hadoop

@ Capco | India - Bengaluru

DevOps Engineer, Data Team

@ SingleStore | Hyderabad, India

Software Engineer (Machine Learning, AI Platform)

@ Phaidra | Remote

Sr. UI/UX Designer - Artificial Intelligence (ID:1213)

@ Truelogic Software | Remote, anywhere in LATAM

Analytics Engineer

@ carwow | London, England, United Kingdom

HRIS Data Analyst

@ SecurityScorecard | Remote