PHASL-NMF: Hierarchical ALS Based Power Non-Negative Matrix Factorization

The non-negative matrix factorization (NMF) has been found an effective clustering algorithm and it outperforms the classical k-means algorithm. Existing researches mainly focus on the problem of reducing the decomposition error between two decomposition matrices and the original matrix. In this paper, inspired by the power k-means algorithm and the hierarchical alternating least square NMF, we propose a novel NMF algorithm called power NMF (PHALS-NMF), which introduces the power mean to reduce decomposition error. Massive experiments on several datasets show the feasibility and effectiveness of PHALS-NMF.