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ICPR
2010
IEEE

Semi-Supervised Distance Metric Learning by Quadratic Programming

9 years 6 months ago
Semi-Supervised Distance Metric Learning by Quadratic Programming
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in lowerdimensional input spaces. We restrict ourselves to pseudo-metrics that are in quadratic forms parameterized by positive semi-definite matrices. The proposed method works in both the input space and kernel induced feature space, and learning distance metric is formulated as a quadratic optimization problem which returns a global optimal solution. Experimental results on several databases show that the learned distance metric improves the performances of the subsequent classification and clustering algorithms.
Hakan Cevikalp
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2010
Where ICPR
Authors Hakan Cevikalp
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