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arXiv · 1601.01422

Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone

Tomoki Matsuzawa·Raissa Relator·Jun Sese·Tsuyoshi Kato
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Abstract

Recently, covariance descriptors have received much attention as powerful representations of set of points. In this research, we present a new metric learning algorithm for covariance descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and runs at O(n^3) time. We empirically demonstrate that randomizing the order of half-spaces in our Dykstra-based algorithm significantly accelerates the convergence to the optimal solution. Furthermore, we show that our approach yields promising experimental results on pattern recognition tasks.

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Tomoki Matsuzawa, Raissa Relator, Jun Sese, Tsuyoshi Kato. 2016-01-07. Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone. https://arxiv.org/abs/1601.01422

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