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Semi-supervised dimensionality reduction using pairwise equivalence constraints
Abstract : To deal with the problem of insufficient labeled data, usually side information -- given in the form of pairwise equivalence constraints between points -- is used to discover groups within data. However, existing methods using side information typically fail in cases with high-dimensional spaces. In this paper, we address the problem of learning from side information for high-dimensional data. To this end, we propose a semi-supervised dimensionality reduction scheme that incorporates pairwise equivalence constraints for finding a better embedding space, which improves the performance of subsequent clustering and classification phases. Our method builds on the assumption that points in a sufficiently small neighborhood tend to have the same label. Equivalence constraints are employed to modify the neighborhoods and to increase the separability of different classes. Experimental results on high-dimensional image data sets show that integrating side information into the dimensionality reduction improves the clustering and classification performance.
Cited literature [15 references]
https://hal.inria.fr/inria-00321053
Contributor : Jakob Verbeek <>
Submitted on : Monday, April 11, 2011 - 11:42:51 AM
Last modification on : Thursday, July 9, 2020 - 9:44:36 AM
Document(s) archivé(s) le : Saturday, December 3, 2016 - 10:49:27 AM
Contributor : Jakob Verbeek <>
Submitted on : Monday, April 11, 2011 - 11:42:51 AM
Last modification on : Thursday, July 9, 2020 - 9:44:36 AM
Document(s) archivé(s) le : Saturday, December 3, 2016 - 10:49:27 AM
