CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Semi-Supervised Classification via Local Spline Regression
Xiang, Shiming1; Nie, Feiping2; Zhang, Changshui2
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2010-11-01
Volume32Issue:11Pages:2039-2053
SubtypeArticle
AbstractThis 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.
KeywordSemi-supervised Classification Local Spline Regression Interactive Image Segmentation
WOS HeadingsScience & Technology ; Technology
WOS KeywordUNLABELED DATA ; DIMENSIONALITY REDUCTION ; LAPLACIAN EIGENMAPS ; SPACE
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000281990900008
Citation statistics
Cited Times:70[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3695
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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