論文

査読有り
2016年

Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors

COMPUTER VISION - ECCV 2016, PT VI
  • Tomoki Matsuzawa
  • ,
  • Raissa Relator
  • ,
  • Jun Sese
  • ,
  • Tsuyoshi Kato

9910
開始ページ
786
終了ページ
799
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-319-46466-4_47
出版者・発行元
SPRINGER INT PUBLISHING AG

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.

リンク情報
DOI
https://doi.org/10.1007/978-3-319-46466-4_47
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000389499900047&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/eccv/eccv2016-6.html#conf/eccv/MatsuzawaRSK16
ID情報
  • DOI : 10.1007/978-3-319-46466-4_47
  • ISSN : 0302-9743
  • Web of Science ID : WOS:000389499900047

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