Supervised bipartite graph inference

Yoshihiro Yamanishi

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

    30 引用 (Scopus)

    抜粋

    We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new method to solve it from the viewpoint of distance metric learning. The method involves the learning of two mappings of the heterogeneous objects to a unified Euclidean space representing the network topology of the bipartite graph, where the graph is easy to infer. The algorithm can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of compound-protein interaction network reconstruction from chemical structure data and genomic sequence data.

    元の言語英語
    ホスト出版物のタイトルAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
    ページ1841-1848
    ページ数8
    出版物ステータス出版済み - 2009
    イベント22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, カナダ
    継続期間: 12 8 200812 11 2008

    出版物シリーズ

    名前Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

    その他

    その他22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
    カナダ
    Vancouver, BC
    期間12/8/0812/11/08

    All Science Journal Classification (ASJC) codes

    • Information Systems

    フィンガープリント Supervised bipartite graph inference' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Yamanishi, Y. (2009). Supervised bipartite graph inference. : Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 1841-1848). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).