A non-convex optimization framework for large-scale low-rank matrix factorization

A non-convex optimization framework for large-scale low-rank matrix factorization

Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in a lower dimensional manifold without a signif...

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Bibliographic Details
Main Authors: Sajad Fathi Hafshejani, Saeed Vahidian, Zahra Moaberfard, Bill Lin
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Machine Learning with Applications
Subjects:
Low-rank
Matrix factorization
Non negative matrix factorization (NMF)
Optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827022001153

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