属性自表达的低秩无监督属性选择算法

doi:10.16088/j.issn.1001-6600.2018.01.008

Abstract

Abstract: A new method of feature selection is proposed to remedy several drawbacks with the existing unsupervised feature reduction method which only utilize the single subspace approach or feature selection method and ignores the inherent correlation within data. Specifically,a novel feature self-representation loss function is proposed to conduct unsupervised learning and feature selection by combining a sparse regularization (l_2,1-norm). And then,subspace learning method and low rank constraint with graph regularization are embeded into the model, to take into account of the global structure and local structure of the data,respectively. Experimental results show that the presented algorithm can achieve better results than some selected comparison algorithms.

Key words: low-rank constraint, feature selection, subspace learning, dimensionality reduction

CLC Number:

TP181

ZHENG Wei,WEN Guoqiu,HE Wei,HU Rongyao,ZHAO Shuzhi. Low-rank Unsupervised Feature Selection Based on Self-representation[J].Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 61-69.

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