Sparse regularization for semi-supervised classification

doi:10.1016/j.patcog.2011.02.013

CSpace > 应用数学研究所

	Sparse regularization for semi-supervised classification
	Fan, Mingyu 1,2; Gu, Nannan 3; Qiao, Hong 3; Zhang, Bo1,2
	2011-08-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	44 期号:8 页码:1777-1784
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.
关键词	Regularization theory Semi-supervised learning Regularized least square classification Dimensionality reduction
DOI	10.1016/j.patcog.2011.02.013
语种	英语
资助项目	NNSF of China[90820007] ; NNSF of China[60725310] ; 863 Program of China[2007AA04Z228] ; 973 Program of China[2007CB311002]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000290054200018
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/12331
专题	应用数学研究所
通讯作者	Zhang, Bo
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.