Sparse regularization for semi-supervised classification | |
Fan, Mingyu1,2; Gu, Nannan3; Qiao, Hong3![]() | |
刊名 | PATTERN RECOGNITION
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2011-08-01 | |
卷号 | 44期号:8页码:1777-1784 |
关键词 | Regularization theory Semi-supervised learning Regularized least square classification Dimensionality reduction |
英文摘要 | Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for classification. It has been shown that the performance of the MR algorithms depends highly on the design of the additional penalty term on manifolds. In this paper, we propose a new approach to define the penalty term on manifolds by the sparse representations instead of the adjacency graphs of data. The process to build this novel penalty term has two steps. First, the best sparse linear reconstruction coefficients for each data point are computed by the l(1)-norm minimization. Secondly, the learner is subject to a cost function which aims to preserve the sparse coefficients. The cost function is utilized as the new penalty term for regularization algorithms. Compared with previous semi-supervised learning algorithms, the new penalty term needs less input parameters and has strong discriminative power for classification. The least square classifier using our novel penalty term is proposed in this paper, which is called the Sparse Regularized Least Square Classification (S-RLSC) algorithm. Experiments on real-world data sets show that our algorithm is very effective. (C) 2011 Elsevier Ltd. All rights reserved. |
WOS标题词 | Science & Technology ; Technology |
类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
研究领域[WOS] | Computer Science ; Engineering |
关键词[WOS] | FACE RECOGNITION ; SIGNAL RECOVERY ; REPRESENTATION ; SYSTEMS |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000290054200018 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/3019] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
作者单位 | 1.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Appl Math, AMSS, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Fan, Mingyu,Gu, Nannan,Qiao, Hong,et al. Sparse regularization for semi-supervised classification[J]. PATTERN RECOGNITION,2011,44(8):1777-1784. |
APA | Fan, Mingyu,Gu, Nannan,Qiao, Hong,&Zhang, Bo.(2011).Sparse regularization for semi-supervised classification.PATTERN RECOGNITION,44(8),1777-1784. |
MLA | Fan, Mingyu,et al."Sparse regularization for semi-supervised classification".PATTERN RECOGNITION 44.8(2011):1777-1784. |
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