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Graph Laplacian kernels for object classification from a single example

Authors Chang, Hong HKUST affiliated (currently or previously)
Yeung, Dit Yan View this author's profile
Issue Date 2006
Source 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, New York, NY, United States, 17 - 22 June 2006, Category numberP2597, Code 68806 , v. 2, 2006, p. 2011-2016
Summary Classification with only one labeled example per class is a challenging problem in machine learning and pattern recognition. While there have been some attempts to address this problem in the context of specific applications, very little work has been done so far on the problem under more general object classification settings. In this paper, we propose a graph-based approach to the problem. Based on a robust path-based similarity measure proposed recently, we construct a weighted graph using the robust path-based similarities as edge weights. A kernel matrix, called graph Laplacian kernel, is then defined based on the graph Laplacian. With the kernel matrix, in principle any kernel-based classifier can be used for classification. In particular, we demonstrate the use of a kernel nearest neighbor classifier on some synthetic data and real-world image sets, showing that our method can successfully solve some difficult classification tasks with only very few labeled examples. © 2006 IEEE.
ISSN 1063-6919
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Language English
Format Conference paper
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