Abstract
Distance metric learning is rather important for measuring the similarity (/dissimilarity) of two instances in many pattern recognition algorithms. Although many linear Mahalanobis metric learning methods can be extended to their kernelized versions for dealing with the nonlinear structure data, choosing the proper kernel and determining the kernel parameters are still tough problems. Furthermore, the single kernel embedded metric is not suited for the problems with multi-view feature representations. In this paper, we address the problem of metric learning with multiple kernels embedding. By analyzing the existing formulations of metric learning with multiple-kernel embedding, we propose a new framework to learn multi-metrics as well as the corresponding weights jointly, the objective function can be shown to be convex and it can be converted to be a multiple kernel learning-support vector machine problem, which can be solved by existing methods. The experiments on single-view and multi-view data show the effectiveness of our method.
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Acknowledgments
The authors would like to thank the anonymous reviewers for providing us constructive suggestions. This work is supported in part by the National Science and Technology Major Project of China (Grant nos. 07-Y30B10-9001-14/16), National Natural Science Foundation of China (Grant nos. 61175075, Grant nos. 61471166), Hunan Provincial Natural Science Foundation (Grant nos. 14JJ2052), Hunan Provincial Postgraduate Innovation Research Foundation (Grant nos. CX2011B145).
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Lu, X., Wang, Y., Zhou, X. et al. A Method for Metric Learning with Multiple-Kernel Embedding. Neural Process Lett 43, 905–921 (2016). https://doi.org/10.1007/s11063-015-9444-3
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DOI: https://doi.org/10.1007/s11063-015-9444-3