Partial least squares regression for graph mining

Hiroto Saigo, Nicole Krämer, Koji Tsuda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

53 被引用数 (Scopus)

抄録

Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm. The mining algorithm is iteratively called with different weight vectors, creating one latent component per one mining call. Our method, graph PLS, is efficient and easy to implement, because the weight vector is updated with elementary matrix calculations. In experiments, our graph PLS algorithm showed competitive prediction accuracies in many chemical datasets and its efficiency was significantly superior to graph boosting (gBoost) and the naive method based on frequent graph mining.

本文言語英語
ホスト出版物のタイトルKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
ページ578-586
ページ数9
DOI
出版ステータス出版済み - 2008
外部発表はい
イベント14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, 米国
継続期間: 8 24 20088 27 2008

出版物シリーズ

名前Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

その他

その他14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
国/地域米国
CityLas Vegas, NV
Period8/24/088/27/08

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム

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