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	Robust Sparse Subspace Learning for Unsupervised Feature Selection
	Wang, Feng ; Rao, Qi ; Zhang, Yongquan ; Chen, Xu
	2016
关键词	FRAMEWORK
英文摘要	Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural learning, but also try to avoid these noises induced during the feature selection process. We utilize the feature structural learning to select the discriminant features and use the robust methods to make selected features more reliable. Furthermore, we propose a new unsupervised feature selection algorithm, namely Robust Sparse Subspace Learning Feature Selection(RSS). And we employ a coordinate descendent algorithm to solve the RSS formulation. Experiments are conducted on several popular datasets to validate the effectiveness of our proposed algorithm and results show that this RSS algorithm achieves better results than traditional feature selection algorithms in most cases.; National Nature Science Foundation of China [61103125, 61373038, 61573157]; Doctoral Fund of Ministry of Education of China [20100141120046]; Humanity and Social Science Fund of Ministry of Education of China [12YJC74008]; Natural Science Foundation of Hubei Province of China [2010CDB08504]; 111 Programme of Introducing Talents of Discipline to Universities [B07037]; CPCI-S(ISTP); 4205-4212
语种	英语
出处	International Joint Conference on Neural Networks (IJCNN)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470189]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wang, Feng,Rao, Qi,Zhang, Yongquan,et al. Robust Sparse Subspace Learning for Unsupervised Feature Selection. 2016-01-01.

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