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Positive semidefinite metric learning with boosting

Conference Paper


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.

Publication Date


  • 2009

Citation


  • Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2009). Positive semidefinite metric learning with boosting. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 1651-1659).

Scopus Eid


  • 2-s2.0-84863356728

Web Of Science Accession Number


Start Page


  • 1651

End Page


  • 1659

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.

Publication Date


  • 2009

Citation


  • Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2009). Positive semidefinite metric learning with boosting. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 1651-1659).

Scopus Eid


  • 2-s2.0-84863356728

Web Of Science Accession Number


Start Page


  • 1651

End Page


  • 1659