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Two-Stage Metric Learning
Authors: Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-maillet and Alexandros Kalousis
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the input data space as the Fisher information distance on the associated statistical manifold. This induces in the input data space a new family of distance metric which presents unique properties. Unlike kernelized metric learning, we do not require the similarity measure to be positive semi-definite. Moreover, it can also be interpreted as a local metric learning algorithm with well defined distance approximation. We evaluate its performance on a number of datasets. It outperforms significantly other metric learning methods and SVM.
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