UDSFS: Unsupervised deep sparse feature selection
文献类型：期刊论文
| 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)
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| 刊名 | Neurocomputing
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| 出版日期 | 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 |
| 产权排序 | 1 |
| 通讯作者 | 丛杨 |
| 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 |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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