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题名: UDSFS: Unsupervised deep sparse feature selection
作者: 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
刊名: Neurocomputing
ISSN号: 0925-2312
出版日期: 2016
卷号: 196, 页码:150-158
收录类别: SCI ; EI
产权排序: 1
项目资助者: 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记录号: WOS:000376543200016
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence
关键词[WOS]: SUPERVISED FEATURE-SELECTION ; MANIFOLD REGULARIZATION ; FACE RECOGNITION ; INFORMATION ; FRAMEWORK
研究领域[WOS]: Computer Science
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17832
Appears in Collections:机器人学研究室_期刊论文

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Recommended Citation:
Cong Y,Wang S,Fan BJ,et al. UDSFS: Unsupervised deep sparse feature selection[J]. Neurocomputing,2016,196:150-158.
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