Kim, Junae; Shen, Chunhua; Wang, Lei
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]
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