CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.11 - November
Issue No.11 - November (2010 vol.32)
Shiming Xiang , Chinese Academy of Sciences, Beijing
Feiping Nie , Tsinghua University, Beijing
Changshui Zhang , Tsinghua University, Beijing
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.35
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.
Semi-supervised classification, local spline regression, interactive image segmentation.
Shiming Xiang, Feiping Nie, Changshui Zhang, "Semi-Supervised Classification via Local Spline Regression", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 11, pp. 2039-2053, November 2010, doi:10.1109/TPAMI.2010.35