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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)
作者部门机器人学研究室
关键词Deep Sparse Feature Selection Machine Learning Group Sparsity Computer Aided Diagnosis
发表期刊Neurocomputing
ISSN0925-2312
2016
卷号196页码:150-158
收录类别SCI ; EI
EI收录号20161302159655
WOS记录号WOS:000376543200016
产权排序1
资助机构NSFC (61375014, 61533015, 61203270) and National Science and Technology Support Program (2012BAI14B03).
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.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence
关键词[WOS]SUPERVISED FEATURE-SELECTION ; MANIFOLD REGULARIZATION ; FACE RECOGNITION ; INFORMATION ; FRAMEWORK
WOS研究方向Computer Science
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/17832
专题机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.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
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.
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