IEICE TRANSACTIONS on Information
Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Tomoki MATSUZAWA
Gunma University
Eisuke ITO
Gunma University
Raissa RELATOR
AIST Artificial Intelligence Research Center
Jun SESE
AIST Artificial Intelligence Research Center
Tsuyoshi KATO
Gunma University
Keyword
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Tomoki MATSUZAWA, Eisuke ITO, Raissa RELATOR, Jun SESE, Tsuyoshi KATO, "Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 4, pp. 849-856, April 2017, doi: 10.1587/transinf.2016EDP7320.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7320/_p
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@ARTICLE{e100-d_4_849,
author={Tomoki MATSUZAWA, Eisuke ITO, Raissa RELATOR, Jun SESE, Tsuyoshi KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors},
year={2017},
volume={E100-D},
number={4},
pages={849-856},
abstract={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.},
keywords={},
doi={10.1587/transinf.2016EDP7320},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Stochastic Dykstra Algorithms for Distance Metric Learning with Covariance Descriptors
T2 - IEICE TRANSACTIONS on Information
SP - 849
EP - 856
AU - Tomoki MATSUZAWA
AU - Eisuke ITO
AU - Raissa RELATOR
AU - Jun SESE
AU - Tsuyoshi KATO
PY - 2017
DO - 10.1587/transinf.2016EDP7320
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2017
AB - 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.
ER -
English
