SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION

Hakan Cevikalp, Roberto Paredes

Abstract

This paper describes a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. As opposed to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield low rank distance metrics. This brings additional benefits such as visualization of data samples and reducing the storage cost, and it is more robust to overfitting since the number of estimated parameters is greatly reduced. Our method works in both the input and kernel induced-feature space, and the distance metric is found by a gradient descent procedure that involves an eigen-decomposition in each step. Experimental results on high-dimensional visual object classification problems show that the computed distance metric improves the performance of the subsequent clustering algorithm.

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Paper Citation


in Harvard Style

Cevikalp H. and Paredes R. (2009). SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 315-322. DOI: 10.5220/0001768903150322


in Bibtex Style

@conference{visapp09,
author={Hakan Cevikalp and Roberto Paredes},
title={SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001768903150322},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION
SN - 978-989-8111-69-2
AU - Cevikalp H.
AU - Paredes R.
PY - 2009
SP - 315
EP - 322
DO - 10.5220/0001768903150322