Reducing the space complexity of a bayes coding algorithm using an expanded context tree

Toshiyasu Matsushima, Shigeich Hirasawa

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

The context tree models are widely used in a lot of research fields. Patricia[7] like trees are applied to the context trees that are expanded according to the increase of the length of a source sequence in the previous researches of non-predictive source coding and model selection. The space complexity of the Patricia like context trees are O(t) where t is the length of a source sequence. On the other hand, the predictive Bayes source coding algorithm cannot use a Patricia like context tree, because it is difficult to hold and update the posterior probability parameters on a Patricia like tree. So the space complexity of the expanded trees in the predictive Bayes coding algorithm is O(t2). In this paper, we propose an efficient predictive Bayes coding algorithm using a new representation of the posterior probability parameters and the compact context tree holding the parameters whose space complexity is O(t).

本文言語English
ホスト出版物のタイトル2009 IEEE International Symposium on Information Theory, ISIT 2009
ページ719-723
ページ数5
DOI
出版ステータスPublished - 2009 11 19
イベント2009 IEEE International Symposium on Information Theory, ISIT 2009 - Seoul, Korea, Republic of
継続期間: 2009 6 282009 7 3

出版物シリーズ

名前IEEE International Symposium on Information Theory - Proceedings
ISSN(印刷版)2157-8102

Conference

Conference2009 IEEE International Symposium on Information Theory, ISIT 2009
国/地域Korea, Republic of
CitySeoul
Period09/6/2809/7/3

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 情報システム
  • モデリングとシミュレーション
  • 応用数学

フィンガープリント

「Reducing the space complexity of a bayes coding algorithm using an expanded context tree」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル