On pairwise kernels: An efficient alternative and generalization analysis

Hisashi Kashima, Satoshi Oyama, Yoshihiro Yamanishi, Koji Tsuda

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

18 被引用数 (Scopus)

抄録

Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance. We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices.

本文言語英語
ホスト出版物のタイトル13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
ページ1030-1037
ページ数8
DOI
出版ステータス出版済み - 2009
外部発表はい
イベント13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, タイ
継続期間: 4月 27 20094月 30 2009

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5476 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
国/地域タイ
CityBangkok
Period4/27/094/30/09

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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