Web: http://arxiv.org/abs/2111.12399

Jan. 24, 2022, 2:11 a.m. | Jeremy E. Cohen

cs.LG updates on arXiv.org arxiv.org

Constrained tensor and matrix factorization models allow to extract
interpretable patterns from multiway data. Therefore identifiability properties
and efficient algorithms for constrained low-rank approximations are nowadays
important research topics. This work deals with columns of factor matrices of a
low-rank approximation being sparse in a known and possibly overcomplete basis,
a model coined as Dictionary-based Low-Rank Approximation (DLRA). While earlier
contributions focused on finding factor columns inside a dictionary of
candidate columns, i.e. one-sparse approximations, this work is the first …

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