Structured metric learning for high dimensional problems

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Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific notions of similarity for the problem at hand. The distance metric learning problem seeks to optimize a distance function subject to constraints that arise from fully-supervised or semi-supervised information. Several recent algorithms have been proposed to learn such distance functions in low dimensional settings. One major shortcoming of these methods is their failure to scale to high dimensional problems that are becoming increasingly ubiquitous in modern data mining applications. In this paper, we present metric learning algorithms that scale linearly with dimensionality, permitting efficient optimization, storage, and evaluation of the learned metric. This is achieved through our main technical contribution which provides a framework based on the log-determinant matrix divergence which enables efficient o...
Jason V. Davis, Inderjit S. Dhillon
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors Jason V. Davis, Inderjit S. Dhillon
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