2021/01/21 by Ran Gu, Qiang Du, Gu, Ran +3
Computer Science · Engineering · Mathematics · #65K10 #90C26 #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #F.2.1 #FOS: Mathematics #G.1.6 #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #acm:65K10 #acm:90C26 #cs.NA #math.NA #math.OC #msc:65K10 #msc:90C26
paper · pdf · doi:10.48550/arxiv.2101.08431
published as Acta Cryst. (2023). A79, 203-216 · 10 pages, 3 figures
arxiv created 2021/01/21 · openalex publication_date 2021/01/21 · openalex created_date 2021/02/01 · arxiv updated 2026/07/31 · openalex updated_date 2026/08/03
In this article, we study algorithms for nonnegative matrix factorization (NMF) in various applications involving streaming data. Utilizing the continual nature of the data, we develop a fast two-stage algorithm for highly efficient and accurate NMF. In the first stage, an alternating non-negative least squares (ANLS) framework is used, in combination with active set method with warm-start strategy for the solution of subproblems. In the second stage, an interior point method is adopted to accelerate the local convergence. The convergence of the proposed algorithm is proved. The new algorithm is compared with some existing algorithms in benchmark tests using both real-world data and synthetic data. The results demonstrate the advantage of our algorithm in finding high-precision solutions.