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Type: Conference paper
Title: Positive Semidefinite Metric Learning with Boosting
Author: Shen, C.
Kim, J.
Wang, L.
Van Den Hengel, A.
Citation: Proceedings of NIPS 2009; pp.1651-1660
Publisher: NIPS
Publisher Place: online
Issue Date: 2009
ISBN: 9781615679119
Conference Name: Annual Conference on Neural Information Processing Systems (23rd : 2009 : Vancouver, Canada)
Statement of
Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel
Abstract: The learning of appropriate distance metrics is a critical problem in image classification and retrieval. 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.
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Computer Science publications

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