IEICE TRANSACTIONS on Information
Frank-Wolfe Algorithm for Learning SVM-Type Multi-Category Classifiers
Summary :
The multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are numerous MC-SVM variants, although different optimization algorithms were developed for diverse learning machines. In this study, we developed a new optimization algorithm that can be applied to several MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction-finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both the direction-finding and line search exist even for the Moreau envelopes of the loss functions. We used several large datasets to demonstrate that the proposed optimization algorithm rapidly converges and thereby improves the pattern recognition performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.11 pp.1923-1929
- Publication Date
- 2021/11/01
- Publicized
- 2021/08/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7025
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Kenya TAJIMA
Gunma University
Yoshihiro HIROHASHI
Esmeraldo ZARA
Gunma University
Tsuyoshi KATO
Gunma University
Keyword
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Kenya TAJIMA, Yoshihiro HIROHASHI, Esmeraldo ZARA, Tsuyoshi KATO, "Frank-Wolfe Algorithm for Learning SVM-Type Multi-Category Classifiers" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 11, pp. 1923-1929, November 2021, doi: 10.1587/transinf.2021EDP7025.
Abstract: The multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are numerous MC-SVM variants, although different optimization algorithms were developed for diverse learning machines. In this study, we developed a new optimization algorithm that can be applied to several MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction-finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both the direction-finding and line search exist even for the Moreau envelopes of the loss functions. We used several large datasets to demonstrate that the proposed optimization algorithm rapidly converges and thereby improves the pattern recognition performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7025/_p
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@ARTICLE{e104-d_11_1923,
author={Kenya TAJIMA, Yoshihiro HIROHASHI, Esmeraldo ZARA, Tsuyoshi KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Frank-Wolfe Algorithm for Learning SVM-Type Multi-Category Classifiers},
year={2021},
volume={E104-D},
number={11},
pages={1923-1929},
abstract={The multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are numerous MC-SVM variants, although different optimization algorithms were developed for diverse learning machines. In this study, we developed a new optimization algorithm that can be applied to several MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction-finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both the direction-finding and line search exist even for the Moreau envelopes of the loss functions. We used several large datasets to demonstrate that the proposed optimization algorithm rapidly converges and thereby improves the pattern recognition performance.},
keywords={},
doi={10.1587/transinf.2021EDP7025},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Frank-Wolfe Algorithm for Learning SVM-Type Multi-Category Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 1923
EP - 1929
AU - Kenya TAJIMA
AU - Yoshihiro HIROHASHI
AU - Esmeraldo ZARA
AU - Tsuyoshi KATO
PY - 2021
DO - 10.1587/transinf.2021EDP7025
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2021
AB - The multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are numerous MC-SVM variants, although different optimization algorithms were developed for diverse learning machines. In this study, we developed a new optimization algorithm that can be applied to several MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction-finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both the direction-finding and line search exist even for the Moreau envelopes of the loss functions. We used several large datasets to demonstrate that the proposed optimization algorithm rapidly converges and thereby improves the pattern recognition performance.
ER -
English
