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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)
Department机器人学研究室
Source PublicationNeurocomputing
ISSN0925-2312
2016
Volume196Pages:150-158
Indexed BySCI ; EI
EI Accession number20161302159655
WOS IDWOS:000376543200016
Contribution Rank1
Funding OrganizationNSFC (61375014, 61533015, 61203270) and National Science and Technology Support Program (2012BAI14B03).
KeywordDeep Sparse Feature Selection Machine Learning Group Sparsity Computer Aided Diagnosis
AbstractIn 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.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence
WOS KeywordSUPERVISED FEATURE-SELECTION ; MANIFOLD REGULARIZATION ; FACE RECOGNITION ; INFORMATION ; FRAMEWORK
WOS Research AreaComputer Science
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17832
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.University of Chinese Academy of Sciences, China
3.College of Automation, Nanjing University of Posts and Telecommunications, China
4.Chinese PLA General Hospital, China
Recommended Citation
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.
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