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A Scalable Algorithm for Learning a Mahalanobis Distance Metric

Kim, Junae; Shen, Chunhua; Wang, Lei

Description

A distance metric that can accurately reflect the intrinsic characteristics of data is critical for visual recognition tasks. An effective solution to defining such a metric is to learn it from a set of training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance. By employing the principle of margin maximization to secure better generalization performances, this algorithm formulates the metric learning as a convex optimization problem with a positive...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
URI: http://hdl.handle.net/1885/55191
Source: Proceedings of Asian Conference on Computer Vision (ACCV 2009)
DOI: 10.1007/978-3-642-12297-2_29

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