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Two Efficient Algorithms for Approximately Orthogonal Nonnegative Matrix Factorization
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DOI：10.1109/LSP.2014.2371895.png)
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Nonnegativematrix 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.
Keyword:
Accelerated proximal gradient
nonnegative matrix factorization
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9.6
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1.1W
被引数:
1.7W
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分享收藏Two algorithms for orthogonal nonnegative matrix factorization with application to clustering两种正交非负矩阵分解算法及其在聚类中的应用.png)
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