A fast Multiplicative Updates algorithm for Non-negative Matrix Factorization

arXiv:2303.17992v2 Announce Type: replace-cross
Abstract: Nonnegative Matrix Factorization is an important tool in unsupervised machine learning to decompose a data matrix into a product of parts that are often interpretable. Many algorithms have been proposed during the last three decades. A well-known method is the Multiplicative Updates algorithm proposed by Lee and Seung in 2002. Multiplicative updates have many interesting features: they are simple to implement and can be adapted to popular variants such as sparse Nonnegative Matrix Factorization, and, …

abstract algorithm algorithms arxiv cs.lg data factorization lee machine machine learning math.oc matrix negative product tool type unsupervised unsupervised machine learning updates