Fast iterative mining using sparsity-inducing loss functions

Hiroto Saigo, Hisashi Kashima, Koji Tsuda

研究成果: Contribution to journalArticle査読

抄録

Apriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.

本文言語英語
ページ(範囲)1766-1773
ページ数8
ジャーナルIEICE Transactions on Information and Systems
E96-D
8
DOI
出版ステータス出版済み - 2013
外部発表はい

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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