Localized centering: Reducing hubness in large-sample data

Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

Hubness has been recently identified as a problematic phenomenon occurring in high-dimensional space. In this paper, we address a different type of hubness that occurs when the number of samples is large. We investigate the difference between the hubness in highdimensional data and the one in large-sample data. One finding is that centering, which is known to reduce the former, does not work for the latter. We then propose a new hub-reduction method, called localized centering. It is an extension of centering, yet works effectively for both types of hubness. Using real-world datasets consisting of a large number of documents, we demonstrate that the proposed method improves the accuracy of knearest neighbor classification.

本文言語English
ホスト出版物のタイトルProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
出版社AI Access Foundation
ページ2645-2651
ページ数7
4
ISBN(電子版)9781577357025
出版ステータスPublished - 2015 6 1
外部発表はい
イベント29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
継続期間: 2015 1 252015 1 30

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
国/地域United States
CityAustin
Period15/1/2515/1/30

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

引用スタイル