Regularized L21-Based Semi-NonNegative Matrix Factorization
Machine Learning
2020-05-12 v1 Numerical Analysis
Numerical Analysis
Machine Learning
Abstract
We present a general-purpose data compression algorithm, Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF). L21 SNF provides robust, parts-based compression applicable to mixed-sign data for which high fidelity, individualdata point reconstruction is paramount. We derive a rigorous proof of convergenceof our algorithm. Through experiments, we show the use-case advantages presentedby L21 SNF, including application to the compression of highly overdeterminedsystems encountered broadly across many general machine learning processes.
Cite
@article{arxiv.2005.04602,
title = {Regularized L21-Based Semi-NonNegative Matrix Factorization},
author = {Anthony D. Rhodes and Bin Jiang},
journal= {arXiv preprint arXiv:2005.04602},
year = {2020}
}