Computer Science ›› 2016, Vol. 43 ›› Issue (5): 204-208.doi: 10.11896/j.issn.1002-137X.2016.05.037

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Orthogonal Non-negative Matrix Factorization for K-means Clustering

LI Meng-jie, XIE Qiang and DING Qiu-lin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The orthogonal NMF K-means clustering algorithm based on basic theory of NMF was proposed to improve the quality of K-means clustering in high-dimensional data.We presented orthogonal NMF algorithm,added orthogonal restraint to data prototype matrix from factorization with improved Gram-Schmidt and Householder orthogonalization separately,which both ensure non-negative of low-dimensional feature and enhance the orthogonality of matrix,and then made K-means clustering.Experimental results show that K-means clustering based on H-ONMF has better clustering results on high-dimensional data.

Key words: High-dimensional data,NMF,Dimension reduction,Orthogonal NMF,K-means clustering

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