Abstract
Metric learning is a hot topic in machine learning. A proper learned metric can measure the similarity between samples better and hence significantly improves the performance of machine learning algorithm. In this paper, we propose a novel enhanced distance metric learning method via Dempster-Shafer (D-S) evidence theory. We consider each instance as an independent source of evidence and combine these pieces of evidence by using Dempster’s rule. Firstly, with reference to the D-S theory, we construct the balanced weight function corresponding to each instance in the metric. Secondly, the novel competitive-cost function is given, which can improve classifier accuracy by narrowing the inner-class distance and increasing the inter-class distance. Finally, we implement a series of experiments on classification by using UCI and face recognition data sets. Experimental results validate that the proposed method can significantly improve the performance of the classifier and the robustness of the algorithm.
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Acknowledgments
This work was supported in part by Shanghai Sailing Program (16YF1404000), the National Natural Science Foundation of China under Grants Nos. 11771276, 11471208 and 11601315.
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Li, Y., Zhang, Y., Peng, Y. (2018). Enhanced Metric Learning via Dempster-Shafer Evidence Theory. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_35
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DOI: https://doi.org/10.1007/978-3-030-04182-3_35
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