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Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors
Tomoki MATSUZAWA Eisuke ITO Raissa RELATOR Jun SESE Tsuyoshi KATO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/04/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Pattern Recognition
covariance descriptor, metric learning, convex optimization, stochastic optimization, Dykstra algorithm,
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In recent years, covariance descriptors have received considerable attention as a strong representation of a set of points. In this research, we propose 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 which runs in O(n3) time. We empirically demonstrate that randomizing the order of half-spaces in the proposed Dykstra-based algorithm significantly accelerates convergence to the optimal solution. Furthermore, we show that the proposed approach yields promising experimental results for pattern recognition tasks.