| Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction | |
| Gao, Xinbo1; Wang, Xiumei1; Tao, Dacheng2; Li, Xuelong3; X. Gao | |
| 2011-04-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
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| ISSN | 1083-4419 |
| 卷号 | 41期号:2页码:425-434 |
| 摘要 | The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method. |
| 文章类型 | Article |
| 关键词 | Dimensionality Reduction Gaussian Process Latent Variable Model (Gp-lvm) Generalized Discriminant Analysis (Gda) Probabilistic Principal Component Analysis (Probabilistic Pca) Supervised Learning |
| 学科领域 | 信号与模式识别 |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TSMCB.2010.2057422 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | PRINCIPAL COMPONENT ANALYSIS ; CLASSIFICATION ; RECOGNITION |
| 语种 | 英语 |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| WOS类目 | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000288454300009 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/8628 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | X. Gao |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore 3.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Xinbo,Wang, Xiumei,Tao, Dacheng,et al. Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2011,41(2):425-434. |
| APA | Gao, Xinbo,Wang, Xiumei,Tao, Dacheng,Li, Xuelong,&X. Gao.(2011).Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,41(2),425-434. |
| MLA | Gao, Xinbo,et al."Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 41.2(2011):425-434. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Gao-IEEE TSMC-B-Supe（1072KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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