2006 MetricLearningByCollapsingClasses
- (Globerson & Roweis, 2006) ⇒ Amir Globerson, and Sam Roweis. (2006). “Metric Learning by Collapsing Classes.” In: Proceedings of Advances in neural information processing systems (NIPS 2005).
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Cited By
2008
- (Xiang et al., 2008) ⇒ Shiming Xiang, Feiping Nie, and Changshui Zhang. (2008). “Learning a Mahalanobis Distance Metric for Data Clustering and Classification.” In: Pattern Recognition, 41. doi:10.1016/j.patcog.2008.05.018
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Abstract
We present an algorithmfor 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.,