Regularized multi-task learning for multi-dimensional log-density gradient estimation (情報論的学習理論と機械学習 情報論的学習理論ワークショップ) Regularized multi-task learning for multi-dimensional log-density gradient estimation

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抄録

Log-density gradient estimation is a fundamental statistical problem and it has various practical applications such as clustering and a measure for non-Gaussianity. A naive two-step approach of first estimating the density and then taking its log-gradient does not perform well because an accurate density estimate does not necessarily lead to an accurate log-density gradient estimate. To cope with this problem, a method to directly estimate the log-density gradient without density estimation was explored. However, even with the direct estimator, high-dimensional log-density gradient estimation is still challenging. In this paper, we propose to apply regularized multi-task learning to direct log-density gradient estimation and show its usefulness experimentally.

収録刊行物

  • 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 114(306), 177-183, 2014-11-17

    一般社団法人電子情報通信学会

各種コード

  • NII論文ID(NAID)
    110009971442
  • NII書誌ID(NCID)
    AA12482480
  • 本文言語コード
    ENG
  • ISSN
    0913-5685
  • NDL 記事登録ID
    025982898
  • NDL 請求記号
    Z16-940
  • データ提供元
    NDL  NII-ELS 
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