Two Efficient Algorithms for Approximately Orthogonal Nonnegative Matrix Factorization
IEEE Signal Process. Lett.（2015）
摘要
Nonnegative matrix factorization (NMF) with orthogonality constraints is quite important due to its close relation with the K-means clustering. While existing algorithms for orthogonal NMF impose strict orthogonality constraints, in this letter we propose a penalty method with the aim of performing approximately orthogonal NMF, together with two efficient algorithms respectively based on the Hierarchical Alternating Least Squares (HALS) and the Accelerated Proximate Gradient (APG) approaches. Experimental evidence was provided to show their high efficiency and flexibility by using synthetic and real-world data.
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关键词
orthogonal nmf,pattern clustering,apg approach,signal processing,penalty method,hierarchical alternating least squares approach,k-means clustering,orthogonality constraints,hals approach,accelerated proximal gradient,orthogonal nonnegative matrix factorization,accelerated proximate gradient approach,least squares approximations,nonnegative matrix factorization,matrix decomposition,gradient methods,clustering algorithms,cost function,acceleration,k means clustering,approximation algorithms,sparse matrices
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