Sparse kernel entropy component analysis for dimensionality reduction of biomedical data
文献类型：期刊论文
| 作者 | Shi, Jun1; Jiang, Qikun1; Zhang, Qi1; Huang, Qinghua2; Li, Xuelong3
|
| 刊名 | neurocomputing
 |
| 出版日期 | 2015-11-30 |
| 卷号 | 168页码:930-940 |
| 关键词 | Sparse kernel entropy component analysis Divide-and-conquer method Dimensionality reduction Biomedical data |
| 英文摘要 | 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. |
| 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] | image ; classification |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000359165000091 |
| 公开日期 | 2015-09-15 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/25284]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。