2016年
Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors
COMPUTER VISION - ECCV 2016, PT VI
- ,
- ,
- ,
- 巻
- 9910
- 号
- 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