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   Vol.E100-D   No.4   pp.849-856
Publication Date: 2017/04/01
Publicized: 2017/01/13
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7320
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.