An Iterative Algorithm for Regularized Non-negative Matrix Factorizations
Machine Learning
2024-10-31 v1 Optimization and Control
Applications
Computation
Abstract
We generalize the non-negative matrix factorization algorithm of Lee and Seung to accept a weighted norm, and to support ridge and Lasso regularization. We recast the Lee and Seung multiplicative update as an additive update which does not get stuck on zero values. We apply the companion R package rnnmf to the problem of finding a reduced rank representation of a database of cocktails.
Cite
@article{arxiv.2410.22698,
title = {An Iterative Algorithm for Regularized Non-negative Matrix Factorizations},
author = {Steven E. Pav},
journal= {arXiv preprint arXiv:2410.22698},
year = {2024}
}
Comments
6 figures