NDLI: Adaptive Metric Learning with the Low Rank Constraint

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Adaptive Metric Learning with the Low Rank Constraint

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Adaptive Metric Learning with the Low Rank Constraint

Content Provider	ACM Digital Library
Author	Yan, Yan Chen, Si Fang, Yuan Gao, Xinbo Wang, Hanzi
Abstract	Good quality distance metrics can significantly promote the performance of many computer vision applications. In order to learn an appropriate distance metric, most of existing metric learning approaches restrict the learned distances between similar pairs to be smaller than a given lower bound, while the learned distances between dissimilar pairs are required to be larger than a given upper bound. However, the learned metrics may not perform well by leveraging the fixed bounds, especially when the data distributions are complex in practical applications. Besides, most methods attempt to learn a distance metric with a full rank matrix transformation from the given training data, which is not only inefficient to compute but also prone to overfitting. In this paper, we propose an Adaptive Metric Learning with the Low Rank Constraint (AML-LR) method, which restricts the learned distances between examples of pairs using adaptive bounds and meanwhile the rank of the learned matrix is minimized. Therefore, the learned metric is adaptive to different data distributions and robust to avoid overfitting. To solve the proposed optimization problem efficiently, we present an effective optimization algorithm based on the accelerated gradient method. Experimental results on UCI datasets and face verification databases demonstrate that AML-LR achieves competitive results compared with other state-of-the-art metric learning methods.
Starting Page	61
Ending Page	65
Page Count	5
File Format	PDF
ISBN	9781450348508
DOI	10.1145/3007669.3007672
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Adaptive bound Low rank Metric learning
Content Type	Text
Resource Type	Article

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