Semi-Supervised Classification via Local Spline Regression

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	Semi-Supervised Classification via Local Spline Regression
	Xiang, Shiming1 ; Nie, Feiping 2; Zhang, Changshui 2
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2010-11-01
卷号	32 期号:11 页码:2039-2053
关键词	Semi-supervised classification local spline regression interactive image segmentation
英文摘要	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.
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]	UNLABELED DATA ; DIMENSIONALITY REDUCTION ; LAPLACIAN EIGENMAPS ; SPACE
收录类别	SCI
语种	英语
WOS记录号	WOS:000281990900008
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3695]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
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
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.