UDSFS: Unsupervised deep sparse feature selection

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	UDSFS: Unsupervised deep sparse feature selection
	Cong Y(丛杨); Wang S(王帅); Fan BJ(范保杰); Yang YS(杨云生); Yu HB(于海斌)
刊名	Neurocomputing
	2016
卷号	196 页码:150-158
关键词	Deep sparse Feature selection Machine learning Group sparsity Computer aided diagnosis
ISSN号	0925-2312
通讯作者	丛杨
产权排序	1
中文摘要	In this paper, we focus on unsupervised feature selection. As we have known, the combination of several feature units into a whole feature vector is broadly adopted for effective object representation, which may inevitably includes some irrelevant/redundant feature units or feature dimensions. Most of the traditional feature selection models can only select the feature dimensions without concerning the intrinsic relationship among different feature units. By taking into consideration the group sparsity of feature dimensions and feature units based on an 2,1 minimization, we propose a new unsupervised feature selection model, unsupervised deep sparse feature selection (UDSFS) in this paper. In comparison with the state-of-the-arts, our UDSFS model can not only select the most discriminative feature units but also assign proper weight to the useful feature dimensions concurrently; moreover, the efficiency and robustness of our UDSFS can be also improved without extracting the discarded irrelevant feature units. For model optimization, we introduce an efficient iterative algorithm to solve the non-smooth, convex model and obtain a global optimization with the convergence rate as O(1/K2) (K is the iteration number). For the experiments, a new medical endoscopic image dataset, Abnormal Endoscopic Image Detection dataset (AEID), is built for evaluation; we also test our model using two public UCI datasets. Various experiments and comparisons with other state-of-the-arts justified the effectiveness and efficiency of our UDSFS model. © 2016 Elsevier B.V.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	SUPERVISED FEATURE-SELECTION ; MANIFOLD REGULARIZATION ; FACE RECOGNITION ; INFORMATION ; FRAMEWORK
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000376543200016
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/17832]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	Cong Y,Wang S,Fan BJ,et al. UDSFS: Unsupervised deep sparse feature selection[J]. Neurocomputing,2016,196:150-158.
APA	Cong Y,Wang S,Fan BJ,Yang YS,&Yu HB.(2016).UDSFS: Unsupervised deep sparse feature selection.Neurocomputing,196,150-158.
MLA	Cong Y,et al."UDSFS: Unsupervised deep sparse feature selection".Neurocomputing 196(2016):150-158.