| Exploiting Local Coherent Patterns for Unsupervised Feature Ranking | |
| Huang, Qinghua1; Tao, Dacheng2; Li, Xuelong3; Jin, Lianwen1; Wei, Gang1 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2011-12-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
 |
| ISSN | 1083-4419 |
| 卷号 | 41期号:6页码:1471-1482 |
| 产权排序 | 3 |
| 摘要 | Prior to pattern recognition, feature selection is often used to identify relevant features and discard irrelevant ones for obtaining improved analysis results. In this paper, we aim to develop an unsupervised feature ranking algorithm that evaluates features using discovered local coherent patterns, which are known as biclusters. The biclusters (viewed as submatrices) are discovered from a data matrix. These submatrices are used for scoring relevant features from two aspects, i.e., the interdependence of features and the separability of instances. The features are thereby ranked with respect to their accumulated scores from the total discovered biclusters before the pattern classification. Experimental results show that this proposed method can yield comparable or even better performance in comparison with the well-known Fisher score, Laplacian score, and variance score using three UCI data sets, well improve the results of gene expression data analysis using gene ontology annotation, and finally demonstrate its advantage of unsupervised feature ranking for high-dimensional data. |
| 文章类型 | Article |
| 关键词 | Bicluster Score Feature Selection Unsupervised Learning |
| 学科领域 | Automation & Control Systems |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TSMCB.2011.2151256 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | FEATURE SUBSET-SELECTION ; CLASSIFICATION ; DISCRIMINANT ; ALGORITHMS |
| 语种 | 英语 |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 项目资助者 | National Basic Research Program of China (973 Program);National Natural Science Funds of China;Specialized Research Funds for the Doctoral Program of Higher Education of China;Fundamental Research Funds for the Central Universities;South China University of Technology;State Key Laboratory of Industrial Control Technology of Zhejiang University |
| WOS类目 | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000297342100003 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/19866 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China 2.Univ Technol, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Qinghua,Tao, Dacheng,Li, Xuelong,et al. Exploiting Local Coherent Patterns for Unsupervised Feature Ranking[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2011,41(6):1471-1482. |
| APA | Huang, Qinghua,Tao, Dacheng,Li, Xuelong,Jin, Lianwen,&Wei, Gang.(2011).Exploiting Local Coherent Patterns for Unsupervised Feature Ranking.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,41(6),1471-1482. |
| MLA | Huang, Qinghua,et al."Exploiting Local Coherent Patterns for Unsupervised Feature Ranking".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 41.6(2011):1471-1482. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Exploiting Local Coh（1382KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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