Semi-supervised dimensionality reduction using pairwise equivalance constraints
Hakan Cevikalp, Jakob Verbeek, Frederic Jurie and Alexander Klaser
In: International Conference on Computer Vision Theory and Applications(2007).
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.