Additive Update Algorithm for Nonnegative Matrix Factorization

TL;DR

This paper introduces an additive update algorithm for nonnegative matrix factorization that offers faster computation compared to the traditional multiplicative update method, enhancing efficiency in data analysis applications.

Contribution

The paper presents a novel additive update algorithm for NMF, improving computational speed over existing multiplicative methods.

Findings

Additive update algorithm outperforms multiplicative in speed.

Faster convergence observed in experiments.

Applicable to large-scale data analysis.

Abstract

Nonnegative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with nonnegative constraints. This problem is currently attracting much attention from researchers for theoretical reasons and for potential applications. Currently, the most popular approach to solve NMF is the multiplicative update algorithm proposed by D.D. Lee and H.S. Seung. In this paper, we propose an additive update algorithm, that has faster computational speed than the algorithm of D.D. Lee and H.S. Seung.