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Joint Learning of Labels and Distance Metric

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5 Author(s)
Bo Liu ; Univ. of Sci. & Technol. of China, Hefei, China ; Meng Wang ; Richang Hong ; Zhengjun Zha
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Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:40 ,  Issue: 3 )