The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
거리 측정법의 차별적 학습이 패턴 인식 성능을 향상시키는 것으로 반복적으로 보고되었습니다. ITML(Information Theoretic Metric Learning) 기반 방법은 Bregman 투영 프레임워크를 거리 측정 최적화에 적용할 수 있다는 장점이 있지만, ITML 기반 방법의 약점은 유사성/상이성 제약 조건에 대한 거리 임계값을 결정해야 한다는 점입니다. 수동으로 일반화 성능이 민감합니다. 본 논문에서는 거리 임계값을 함께 최적화하는 새로운 메트릭 학습 알고리즘 공식을 제시합니다. 최적화는 여전히 Bregman 투영 프레임워크에 있으므로 Dykstra 알고리즘을 최적화에 적용할 수 있습니다. 각 반복에서 솔루션을 절반 공간에 투영하려면 비선형 방정식을 풀어야 합니다. 우리는 절반 공간에 투영하기 위한 효율적인 기술을 개발했습니다. 우리는 제안된 메트릭 학습 알고리즘에 대해 거리 임계값이 자동으로 조정되지만 제안하는 알고리즘의 패턴 인식 정확도는 기존 메트릭 학습 방법과 비슷하거나 비슷하다는 것을 경험적으로 보여줍니다.
Rachelle RIVERO
Gunma University, Graduate School of Science and Technology,University of the Philippines
Yuya ONUMA
Gunma University, Graduate School of Science and Technology
Tsuyoshi KATO
Gunma University, Graduate School of Science and Technology,Gunma University,Waseda University
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Rachelle RIVERO, Yuya ONUMA, Tsuyoshi KATO, "Threshold Auto-Tuning Metric Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 6, pp. 1163-1170, June 2019, doi: 10.1587/transinf.2018EDP7145.
Abstract: It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. Although the ITML (Information Theoretic Metric Learning)-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric, a weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually, onto which the generalization performance is sensitive. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. We have developed an efficient technique for projection onto a half-space. We empirically show that although the distance threshold is automatically tuned for the proposed metric learning algorithm, the accuracy of pattern recognition for the proposed algorithm is comparable, if not better, to the existing metric learning methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7145/_p
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@ARTICLE{e102-d_6_1163,
author={Rachelle RIVERO, Yuya ONUMA, Tsuyoshi KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Threshold Auto-Tuning Metric Learning},
year={2019},
volume={E102-D},
number={6},
pages={1163-1170},
abstract={It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. Although the ITML (Information Theoretic Metric Learning)-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric, a weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually, onto which the generalization performance is sensitive. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. We have developed an efficient technique for projection onto a half-space. We empirically show that although the distance threshold is automatically tuned for the proposed metric learning algorithm, the accuracy of pattern recognition for the proposed algorithm is comparable, if not better, to the existing metric learning methods.},
keywords={},
doi={10.1587/transinf.2018EDP7145},
ISSN={1745-1361},
month={June},}
부
TY - JOUR
TI - Threshold Auto-Tuning Metric Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1163
EP - 1170
AU - Rachelle RIVERO
AU - Yuya ONUMA
AU - Tsuyoshi KATO
PY - 2019
DO - 10.1587/transinf.2018EDP7145
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2019
AB - It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. Although the ITML (Information Theoretic Metric Learning)-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric, a weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually, onto which the generalization performance is sensitive. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. We have developed an efficient technique for projection onto a half-space. We empirically show that although the distance threshold is automatically tuned for the proposed metric learning algorithm, the accuracy of pattern recognition for the proposed algorithm is comparable, if not better, to the existing metric learning methods.
ER -