2017/10/29 by Andri Mirzal, Mirzal, Andri
Computer Science · #65F30 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Matrix Theory and Algorithms #cs.LG #msc:65F30
paper · pdf · doi:10.48550/arxiv.1710.11478
arXiv admin note: substantial text overlap with arXiv:1010.5290
openalex publication_date 2017/10/29 · arxiv created 2018/11/15 · arxiv updated 2018/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A convergent algorithm for nonnegative matrix factorization with orthogonality constraints imposed on both factors is proposed in this paper. This factorization concept was first introduced by Ding et al. with intent to further improve clustering capability of NMF. However, as the original algorithm was developed based on multiplicative update rules, the convergence of the algorithm cannot be guaranteed. In this paper, we utilize the technique presented in our previous work to develop the algorithm and prove that it converges to a stationary point inside the solution space.