Spectrum-Based Kernel Length Estimation for Gaussian Process Classification
文献类型：期刊论文
| 作者 | Wang, Liang1 ; Li, Chuan2
|
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2014-06-01 |
| 卷号 | 44期号:6页码:805-816 |
| 关键词 | Autocorrelation Gaussian process classification kernel length scale estimation spectrum analysis |
| 英文摘要 | Recent studies have shown that Gaussian process (GP) classification, a discriminative supervised learning approach, has achieved competitive performance in real applications compared with most state-of-the-art supervised learning methods. However, the problem of automatic model selection in GP classification, involving the kernel function form and the corresponding parameter values (which are unknown in advance), remains a challenge. To make GP classification a more practical tool, this paper presents a novel spectrum analysis-based approach for model selection by refining the GP kernel function to match the given input data. Specifically, we target the problem of GP kernel length scale estimation. Spectrums are first calculated analytically from the kernel function itself using the autocorrelation theorem as well as being estimated numerically from the training data themselves. Then, the kernel length scale is automatically estimated by equating the two spectrum values, i.e., the kernel function spectrum equals to the estimated training data spectrum. Compared with the classical Bayesian method for kernel length scale estimation via maximizing the marginal likelihood (which is time consuming and could suffer from multiple local optima), extensive experimental results on various data sets show that our proposed method is both efficient and accurate. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | SPACED DATA |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000337960000007 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3786]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Max Planck Inst Informat, D-66123 Saarbrucken, Germany |
| 推荐引用方式 GB/T 7714 | Wang, Liang,Li, Chuan. Spectrum-Based Kernel Length Estimation for Gaussian Process Classification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(6):805-816. |
| APA | Wang, Liang,&Li, Chuan.(2014).Spectrum-Based Kernel Length Estimation for Gaussian Process Classification.IEEE TRANSACTIONS ON CYBERNETICS,44(6),805-816. |
| MLA | Wang, Liang,et al."Spectrum-Based Kernel Length Estimation for Gaussian Process Classification".IEEE TRANSACTIONS ON CYBERNETICS 44.6(2014):805-816. |
入库方式： OAI收割
来源：自动化研究所
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