Distance metric learning with eigenvalue fine tuning

Wenquan Wang, Ya Zhang, Jinglu Hu

研究成果: Conference contribution

抄録

Distance metric learning focuses on learning one global or multiple local distance functions to draw similar instances close to each other and push away dissimilar ones. Most existing work has to do matrix projection to learn distance functions. In this paper, we present a novel distance function learning model which is based on eigenvalue fine tuning. Our model not only is able to learn the global distance function but also can be easily adopted into local metric learning tasks. From the perspective of dimension reduction, the proposed model can measure how much information has been preserved after feature transformation. Moreover, we connect our model with principal components analysis to improve its performance by introducing the label information. Experimental results have demonstrated the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトル2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ502-509
ページ数8
ISBN(電子版)9781509061815
DOI
出版ステータスPublished - 2017 6 30
イベント2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
継続期間: 2017 5 142017 5 19

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period17/5/1417/5/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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