Submodular fractional programming for balanced clustering

Yoshinobu Kawahara, Kiyohito Nagano, Yoshio Okamoto

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)

抄録

We address the balanced clustering problem where cluster sizes are regularized with submodular functions. The objective function for balanced clustering is a submodular fractional function, i.e; the ratio of two submodular functions, and thus includes the well-known ratio cuts as special cases. In this paper, we present a novel algorithm for minimizing this objective function (submodular fractional programming) using recent submodular optimization techniques. The main idea is to utilize an algorithm to minimize the difference of two submodular functions, combined with the discrete Newton method. Thus, it can be applied to the objective function involving any submodular functions in both the numerator and the denominator, which enables us to design flexible clustering setups. We also give theoretical analysis on the algorithm, and evaluate the performance through comparative experiments with conventional algorithms by artificial and real datasets.

本文言語英語
ページ(範囲)235-243
ページ数9
ジャーナルPattern Recognition Letters
32
2
DOI
出版ステータス出版済み - 1月 15 2011
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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