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Metric Learning with Covariance Descriptors |
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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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References |
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Tomoki Matsuzawa, Eisuke Ito, Raissa Relator, Jun Sese, Tsuyoshi Kato, Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors, IEICE Transactions on Information & Systems, Vol.E100-D,No.4,pp.-,Apr. 2017. |
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Tomoki Matsuzawa, Raissa Relator, Jun Sese, Tsuyoshi Kato, "Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors" The 14th European Conference on Computer Vision (ECCV2016) – Amsterdam, The Netherlands, published in Computer Vision - ECCV2016, Lecture Notes in Computer Science (LNCS), ISBN 978-3-319-46466-4, pp. 786-799. . [pdf][bibtex][japanese] |
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Rachelle Rivero, Yuya Onuma, Tsuyoshi Kato, Threshold Auto-Tuning Metric Learning, IEICE Transactions on Information & Systems, Vol.E102-D,No.06,pp.-,Jun. 2019. |
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