Sparse Compositional Metric Learning

Abstract : We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned met-rics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.
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Communication dans un congrès
AAAI Conference on Artificial Intelligence (AAAI 2014), Jul 2014, Quebec City, Canada. 〈https://www.aaai.org/Conferences/AAAI/aaai14.php〉
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  • HAL Id : hal-01430847, version 1

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Yuan Shi, Aurélien Bellet, Fei Sha. Sparse Compositional Metric Learning. AAAI Conference on Artificial Intelligence (AAAI 2014), Jul 2014, Quebec City, Canada. 〈https://www.aaai.org/Conferences/AAAI/aaai14.php〉. 〈hal-01430847〉

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