TY - GEN
T1 - Efficient max-margin metric learning
AU - Xiong, Caiming
AU - Johnson, David
AU - Corso, Jason
PY - 2012
Y1 - 2012
N2 - Efficient learning of an appropriate distance metric is an increasingly important problem in machine learning. However, current methods are limited by scalability issues or are unsuited to use with general similarity/dissimilarity constraints. In this paper, we propose an efficient metric learning method based on the max-margin framework with pairwise constraints that has strong generalization gu arantees. First, we reformulate the max-margin metric learning problem as a structured support vector machine which we can optimize in linear time via a cutting-plane method. Second, we propose a kernelized extension to the method, with a linear-time-computable approximation based on a matching pursuit algorithm. We find our method to be comparable to or better than state of the art metric learning techniques at a number of machine learning and computer vision classification tasks.
AB - Efficient learning of an appropriate distance metric is an increasingly important problem in machine learning. However, current methods are limited by scalability issues or are unsuited to use with general similarity/dissimilarity constraints. In this paper, we propose an efficient metric learning method based on the max-margin framework with pairwise constraints that has strong generalization gu arantees. First, we reformulate the max-margin metric learning problem as a structured support vector machine which we can optimize in linear time via a cutting-plane method. Second, we propose a kernelized extension to the method, with a linear-time-computable approximation based on a matching pursuit algorithm. We find our method to be comparable to or better than state of the art metric learning techniques at a number of machine learning and computer vision classification tasks.
KW - Cutting-Plane
KW - Matching Pursuit
KW - Metric Learning
KW - Structured SVM
UR - http://www.scopus.com/inward/record.url?scp=84887488273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887488273&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84887488273
SN - 9789728939694
T3 - Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012
SP - 115
EP - 123
BT - Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012
T2 - IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012, Part of the IADIS Multi Conference on Computer Science and Information Systems 2012, MCCSIS 2012
Y2 - 21 July 2012 through 23 July 2012
ER -