| 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)
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| Department | 机器人学研究室 |
| Source Publication | Neurocomputing
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| ISSN | 0925-2312 |
| 2016 | |
| Volume | 196Pages:150-158 |
| Indexed By | SCI ; EI |
| EI Accession number | 20161302159655 |
| WOS ID | WOS:000376543200016 |
| Contribution Rank | 1 |
| Funding Organization | NSFC (61375014, 61533015, 61203270) and National Science and Technology Support Program (2012BAI14B03). |
| Keyword | Deep Sparse Feature Selection Machine Learning Group Sparsity Computer Aided Diagnosis |
| Abstract | 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. |
| Language | 英语 |
| WOS Headings | Science & Technology ; Technology |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS Keyword | SUPERVISED FEATURE-SELECTION ; MANIFOLD REGULARIZATION ; FACE RECOGNITION ; INFORMATION ; FRAMEWORK |
| WOS Research Area | Computer Science |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/17832 |
| Collection | 机器人学研究室 |
| Corresponding Author | Cong Y(丛杨) |
| Affiliation | 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 |
| 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| UDSFS_ Unsupervised （1003KB） | 期刊论文 | 作者接受稿 | 开放获取 | ODC PDDL | View Application Full Text | |
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