Metric Learning by Collapsing Classes

by Amir Globerson , Sam Roweis

Citations:

84 - 2 self

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Datum	Value	Source
TITLE	Metric Learning by Collapsing Classes	user correction - Legacy Corrections
AUTHOR NAME	Amir Globerson	user correction
AUTHOR AFFIL	School of Computer Science and Engineering,; Interdisciplinary Center for Neural Computation	user correction
AUTHOR ADDR	The Hebrew University Jerusalem, 91904, Israel	user correction
AUTHOR NAME	Sam Roweis	user correction
AUTHOR AFFIL	Machine Learning Group; Department of Computer Science; University of Toronto	user correction
AUTHOR ADDR	Canada	user correction
ABSTRACT	We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one under which points in the same class are simultaneously near each other and far from points in the other classes. We construct a convex optimization problem whose solution generates such a metric by trying to collapse all examples in the same class to a single point and push examples in other classes infinitely far away. We show that when the metric we learn is used in simple classifiers, it yields substantial improvements over standard alternatives on a variety of problems. We also discuss how the learned metric may be used to obtain a compact low dimensional feature representation of the original input space, allowing more efficient classification with very little reduction in performance.	user correction - Legacy Corrections
CITATIONS	10 found	ParsCit 1.0