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Regularized Distance Metric Learning:Theory and Algorithm
Rong Jin · Shijun Wang · Yang Zhou

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None

In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric learning. Our empirical studies with data classification and face recognition show that the proposed algorithm is (i) effective for distance metric learning when compared to the state-of-the-art methods, and (ii) efficient and robust for high dimensional data.

Author Information

Rong Jin (Michigan State University (MSU))
Shijun Wang
Yang Zhou

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