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Positive Semidefinite Metric Learning with Boosting
Chunhua Shen · Junae Kim · Lei Wang · Anton van den Hengel

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @

The learning of appropriate distance metrics is a critical problem in classification. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.

Author Information

Chunhua Shen (University of Adelaide)
Junae Kim (The Australian National University)
Lei Wang (University of Wollongong)
Anton van den Hengel (University of Adelaide)

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