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 …


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