Authors:
Hakan Cevikalp
;
Jakob Verbeek
;
Frédéric Jurie
and
Alexander Kläser
Affiliation:
Inria Rhone-Alpes, France
Keyword(s):
Constrained clustering, dimensionality reduction, image segmentation, metric learning, pairwise constraints,
semi-supervised learning, spectral clustering.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
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 re
duction improves the clustering and classification performance.
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