Dictionary-based Low-Rank Approximations and the Mixed Sparse Coding problem. (arXiv:2111.12399v2 [cs.LG] UPDATED)

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 …

arxiv coding dictionary mixed