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Conference Papers Year : 2008

Semi-supervised dimensionality reduction using pairwise equivalence constraints


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.
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Dates and versions

inria-00321053 , version 1 (25-01-2011)
inria-00321053 , version 2 (11-04-2011)


  • HAL Id : inria-00321053 , version 2


Hakan Cevikalp, Jakob Verbeek, Frédéric Jurie, Alexander Klaser. Semi-supervised dimensionality reduction using pairwise equivalence constraints. VISAPP '08 - 3rd International Conference on Computer Vision Theory and Applications, Jan 2008, Funchal, Portugal. pp.489-496. ⟨inria-00321053v2⟩
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