Journal
PATTERN RECOGNITION
Volume 45, Issue 2, Pages 844-862Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.07.026
Keywords
Distance metric learning; Hypothesis margins; Boosting approaches
Funding
- National Science Council [NSC 99-2221-E-019-036]
- [NTOU-RD981-05-02-04-01]
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Determining a proper distance metric is often a crucial step for machine learning. In this paper, a boosting algorithm is proposed to learn a Mahalanobis distance metric. Similar to most boosting algorithms, the proposed algorithm improves a loss function iteratively. In particular, the loss function is defined in terms of hypothesis margins, and a metric matrix base-learner specific to the boosting framework is also proposed. Experimental results show that the proposed approach can yield effective Mahalanobis distance metrics for a variety of data sets, and demonstrate the feasibility of the proposed approach. (C) 2011 Elsevier Ltd. All rights reserved.
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