Semi-Supervised Classification via Local Spline Regression

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Semi-Supervised Classification via Local Spline Regression
	Xiang, Shiming1 ; Nie, Feiping 2; Zhang, Changshui 2
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2010-11-01
卷号	32 期号:11 页码:2039-2053
文章类型	Article
摘要	This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions. It is smooth, nonlinear, and able to interpolate the scattered data points with high accuracy. Specifically, in each neighborhood, an optimal spline is estimated via regularized least squares regression. With this spline, each of the neighboring data points is mapped to be a class label. Then, the regularized loss is evaluated and further formulated in terms of class label vector. Finally, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on the labeled and unlabeled data. To achieve the goal of semi-supervised classification, an objective function is constructed by combining together the global loss of the local spline regressions and the squared errors of the class labels of the labeled data. In this way, a transductive classification algorithm is developed in which a globally optimal classification can be finally obtained. In the semi-supervised learning setting, the proposed algorithm is analyzed and addressed into the Laplacian regularization framework. Comparative classification experiments on many public data sets and applications to interactive image segmentation and image matting illustrate the validity of our method.
关键词	Semi-supervised Classification Local Spline Regression Interactive Image Segmentation
WOS标题词	Science & Technology ; Technology
关键词[WOS]	UNLABELED DATA ; DIMENSIONALITY REDUCTION ; LAPLACIAN EIGENMAPS ; SPACE
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000281990900008
引用统计	被引频次：98[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3695
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Xiang, Shiming,Nie, Feiping,Zhang, Changshui. Semi-Supervised Classification via Local Spline Regression[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2010,32(11):2039-2053.
APA	Xiang, Shiming,Nie, Feiping,&Zhang, Changshui.(2010).Semi-Supervised Classification via Local Spline Regression.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,32(11),2039-2053.
MLA	Xiang, Shiming,et al."Semi-Supervised Classification via Local Spline Regression".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 32.11(2010):2039-2053.