抄録
The distance metric learning is the method to learn the distance metric from training data considering its statistical characteristics under the arbitrary constraints. To obtain the desirable distance metric, the optimization problem is solved. Most of the distance metric learning methods aim to gain the global optimal metric matrix. However there is a possibility that the global metric matrix cannot express the statistical characteristics of each category in detail. In addition, if the dimension of input data increase, the computational cost of calculating distance between data increases either. To avoid this problem, we adopt the way to use the l1 regularization to gain sparse metric matrix. By combining those, we focus on the way to deriving the plural metric matrices with a sparse structure in this study. To verify the effective ness of our proposed method, we conduct simulation experiments by using UCI machine learning repository.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 285-289 |
| ページ数 | 5 |
| ISBN（電子版） | 9784885523090 |
| 出版物ステータス | Published - 2017 2 2 |
| イベント | 3rd International Symposium on Information Theory and Its Applications, ISITA 2016 - Monterey, United States 継続期間: 2016 10 30 → 2016 11 2 |
Other
| Other | 3rd International Symposium on Information Theory and Its Applications, ISITA 2016 |
|---|---|
| 国 | United States |
| 市 | Monterey |
| 期間 | 16/10/30 → 16/11/2 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems
- Signal Processing
- Library and Information Sciences
これを引用
Distance metric learning based on different ℓ1 regularized metric matrices in each category. / Mikawa, Kenta; Kobayashi, Manabu; Goto, Masayuki; Hirasawa, Shigeichi.
Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 285-289 7840431.研究成果: Conference contribution
}
TY - GEN
T1 - Distance metric learning based on different ℓ1 regularized metric matrices in each category
AU - Mikawa, Kenta
AU - Kobayashi, Manabu
AU - Goto, Masayuki
AU - Hirasawa, Shigeichi
PY - 2017/2/2
Y1 - 2017/2/2
N2 - The distance metric learning is the method to learn the distance metric from training data considering its statistical characteristics under the arbitrary constraints. To obtain the desirable distance metric, the optimization problem is solved. Most of the distance metric learning methods aim to gain the global optimal metric matrix. However there is a possibility that the global metric matrix cannot express the statistical characteristics of each category in detail. In addition, if the dimension of input data increase, the computational cost of calculating distance between data increases either. To avoid this problem, we adopt the way to use the l1 regularization to gain sparse metric matrix. By combining those, we focus on the way to deriving the plural metric matrices with a sparse structure in this study. To verify the effective ness of our proposed method, we conduct simulation experiments by using UCI machine learning repository.
AB - The distance metric learning is the method to learn the distance metric from training data considering its statistical characteristics under the arbitrary constraints. To obtain the desirable distance metric, the optimization problem is solved. Most of the distance metric learning methods aim to gain the global optimal metric matrix. However there is a possibility that the global metric matrix cannot express the statistical characteristics of each category in detail. In addition, if the dimension of input data increase, the computational cost of calculating distance between data increases either. To avoid this problem, we adopt the way to use the l1 regularization to gain sparse metric matrix. By combining those, we focus on the way to deriving the plural metric matrices with a sparse structure in this study. To verify the effective ness of our proposed method, we conduct simulation experiments by using UCI machine learning repository.
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M3 - Conference contribution
AN - SCOPUS:85015158705
SP - 285
EP - 289
BT - Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
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