| Sparse kernel entropy component analysis for dimensionality reduction of biomedical data | |
Shi, Jun1; Jiang, Qikun1; Zhang, Qi1; Huang, Qinghua2; Li, Xuelong3
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| 2015-11-30 | |
| 发表期刊 | NEUROCOMPUTING
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| 卷号 | 168页码:930-940 |
| 摘要 | Dimensionality reduction is ubiquitous in biomedical applications. A newly proposed spectral dimensionality reduction method, named kernel entropy component analysis (KECA), can reveal the structure related to Renyi entropy of an input space data set. However, each principal component in the Hilbert space depends on all training samples in KECA, causing degraded performance. To overcome this drawback, a sparse KECA (SKECA) algorithm based on a recursive divide-and-conquer (DC) method is proposed in this work. The original large and complex problem of KECA is decomposed into a series of small and simple sub-problems, and then they are solved recursively. The performance of SKECA is evaluated on four biomedical datasets, and compared with KECA, principal component analysis (PCA), kernel PCA (KPCA), sparse PCA and sparse KPCA. Experimental results indicate that the SKECA outperforms conventional dimensionality reduction algorithms, even for high order dimensional features. It suggests that SKECA is potentially applicable to biomedical data processing. (C) 2015 Elsevier B.V. All rights reserved. |
| 文章类型 | Article |
| 关键词 | Sparse Kernel Entropy Component Analysis Divide-and-conquer Method Dimensionality Reduction Biomedical Data |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2015.05.032 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | IMAGE ; CLASSIFICATION |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000359165000091 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/25284 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Shanghai Univ, Sch Commun & Informat Engn, Shanghai, Peoples R China 2.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Jun,Jiang, Qikun,Zhang, Qi,et al. Sparse kernel entropy component analysis for dimensionality reduction of biomedical data[J]. NEUROCOMPUTING,2015,168:930-940. |
| APA | Shi, Jun,Jiang, Qikun,Zhang, Qi,Huang, Qinghua,&Li, Xuelong.(2015).Sparse kernel entropy component analysis for dimensionality reduction of biomedical data.NEUROCOMPUTING,168,930-940. |
| MLA | Shi, Jun,et al."Sparse kernel entropy component analysis for dimensionality reduction of biomedical data".NEUROCOMPUTING 168(2015):930-940. |
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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 | ||
| Sparse kernel entrop（1811KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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