Quadratic regularization projected Barzilai–Borwein method for nonnegative matrix factorization

• Autores: Yakui Huang, Hongwei Liu, Shuisheng Zhou
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 29, Nº 6, 2015, págs. 1665-1684
• Idioma: inglés
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• Resumen

  • In this paper, based on the alternating nonnegative least squares framework, we present a new efficient method for nonnegative matrix factorization that uses a quadratic regularization projected Barzilai–Borwein (QRPBB) method to solve the subproblems. At each iteration, the QRPBB method first generates a point by solving a strongly convex quadratic minimization problem, which has a simple closed-form solution that is inexpensive to calculate, and then applies a projected Barzilai–Borwein method to update the solution of NMF. Global convergence result is established under mild conditions. Numerical comparisons of methods on both synthetic and real-world datasets show that the proposed method is efficient.