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Title of Journal: Soft Comput

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Abbravation: Soft Computing

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Springer Berlin Heidelberg

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DOI

10.1002/micr.1920150116

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1433-7479

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Metric learning with geometric mean for similariti

Authors: Huibing Wang Lin Feng Yang Liu
Publish Date: 2015/12/21
Volume: 20, Issue: 10, Pages: 3969-3979
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Abstract

Distance metric learning aims to find an appropriate method to measure similarities between samples An excellent distance metric can greatly improve the performance of many machine learning algorithms Most previous methods in this area have focused on finding metrics which utilize largemargin criterion to optimize compactness and separability simultaneously One major shortcoming of these methods is their failure to balance all betweenclass scatters when the distributions of samples are extremely unbalanced Largemargin criterion tends to maintain bigger scatters while abandoning those smaller ones to make the total scatters maximized In this paper we introduce a regularized metric learning framework metric learning with geometric mean which obtains a distance metric using geometric mean The novel method balances all betweenclass scatters and separates samples from different classes simultaneously Various experiments on benchmark datasets show the good performance of the novel methodThis study was funded by National Natural Science Foundation of People’s Republic of China 61173163 61370200 Huibing Wang Lin Feng and Yang Liu declare that they have no conflict of interest This article does not contain any studies with human participants or animals performed by any of the authors

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