Journal
PATTERN RECOGNITION
Volume 81, Issue -, Pages 562-574Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.04.024
Keywords
Distance metric learning; Nearest neighbor; Linear transformation; DC programming
Funding
- Special Research Fund Doctoral Scholarships, Ghent University, Belgium [BOF15/DOS/039]
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Distance metric learning has motivated a great deal of research over the last years due to its robustness for many pattern recognition problems. In this paper, we develop a supervised distance metric learning method that aims to improve the performance of nearest-neighbor classification. Our method is inspired by the large-margin principle, resulting in an objective function based on a sum of margin violations to be minimized. Due to the use of the ramp loss function, the corresponding objective function is nonconvex, making it more challenging. To overcome this limitation, we formulate our distance metric learning problem as an instance of difference of convex functions (DC) programming. This allows us to design a more robust method than when using standard optimization techniques. The effectiveness of this method is empirically demonstrated through extensive experiments on several standard benchmark data sets. (C) 2018 Elsevier Ltd. All rights reserved.
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