Matrix-wise $\ell_0$-constrained Sparse Nonnegative Least Squares. (arXiv:2011.11066v4 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2011.11066

Nonnegative least squares problems with multiple right-hand sides (MNNLS)
arise in models that rely on additive linear combinations. In particular, they
are at the core of most nonnegative matrix factorization algorithms and have
many applications. The nonnegativity constraint is known to naturally favor
sparsity, that is, solutions with few non-zero entries. However, it is often
useful to further enhance this sparsity, as it improves the interpretability of
the results and helps reducing noise, which leads to the sparse MNNLS problem. …

arxiv least lg squares