### 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 l_{1} 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 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Hardware and Architecture
- Information Systems
- Signal Processing
- Library and Information Sciences

### これを引用

_{1}regularized metric matrices in each category. ：

*Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016*(pp. 285-289). [7840431] Institute of Electrical and Electronics Engineers Inc..

**Distance metric learning based on different ℓ _{1} regularized metric matrices in each category.** / Mikawa, Kenta; Kobayashi, Manabu; Goto, Masayuki; Hirasawa, Shigeichi.

研究成果: Conference contribution

_{1}regularized metric matrices in each category. ：

*Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016.*, 7840431, Institute of Electrical and Electronics Engineers Inc., pp. 285-289, 3rd International Symposium on Information Theory and Its Applications, ISITA 2016, Monterey, United States, 16/10/30.

_{1}regularized metric matrices in each category. ： 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

}

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.

UR - http://www.scopus.com/inward/record.url?scp=85015158705&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015158705&partnerID=8YFLogxK

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.

ER -