Additive Update Algorithm for Nonnegative Matrix Factorization
This work addresses the need for faster NMF algorithms, which is important for data analysis applications, but the improvement is incremental.
The authors propose an additive update algorithm for nonnegative matrix factorization that achieves faster computational speed than the standard multiplicative update algorithm.
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.