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Proceedings Paper

Semi-supervised dimensionality reduction for image retrieval
Author(s): Bin Zhang; Yangqiu Song; Wenjun Yin; Ming Xie; Jin Dong; Changshui Zhang
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Paper Abstract

This paper proposes a novel semi-supervised dimensionality reduction learning algorithm for the ranking problem. Generally, we do not make the assumption of existence of classes and do not want to find the classification boundaries. Instead, we only assume that the data point cloud can construct a graph which describes the manifold structure, and there are multiple concepts on different parts of the manifold. By maximizing the distance between different concepts and simultaneously preserving the local structure on the manifold, the learned metric can indeed give good ranking results. Moreover, based on the theoretical analysis of the relationship between graph Laplacian and manifold Laplace-Beltrami operator, we develop an online learning algorithm that can incrementally learn the unlabeled data.

Paper Details

Date Published: 28 January 2008
PDF: 9 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 682225 (28 January 2008); doi: 10.1117/12.767197
Show Author Affiliations
Bin Zhang, IBM China Research Lab. (China)
Yangqiu Song, Tsinghua Univ. (China)
Wenjun Yin, IBM China Research Lab. (China)
Ming Xie, IBM China Research Lab. (China)
Jin Dong, IBM China Research Lab. (China)
Changshui Zhang, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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