## 抄録

In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

本文言語 | English |
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ホスト出版物のタイトル | Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 |

出版社 | Institute of Electrical and Electronics Engineers Inc. |

ページ | 1645-1650 |

ページ数 | 6 |

ISBN（印刷版） | 9781479986965 |

DOI | |

出版ステータス | Published - 2016 1月 12 |

イベント | IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong 継続期間: 2015 10月 9 → 2015 10月 12 |

### Other

Other | IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 |
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国/地域 | Hong Kong |

City | Kowloon Tong |

Period | 15/10/9 → 15/10/12 |

## ASJC Scopus subject areas

- 人工知能
- コンピュータ ネットワークおよび通信
- エネルギー工学および電力技術
- 情報システムおよび情報管理
- 制御およびシステム工学