抄録
This paper presents two clustering algorithms based on α-divergence between memberships and variables that control cluster sizes: one is for spherical data and the other for categorical multivariate data. First, this paper shows that a conventional method for vectorial data can be interpreted as the regularization of another conventional method with α-divergence. Second, with this interpretation, a spherical clustering algorithm based on α-divergence is derived from an optimization problem built by regularizing a conventional method with α-divergence. Third, this paper connects the facts that the α-divergence is a generalization of Kullback-Leibler (KL)-divergence, and that three conventional co-clustering methods are based on KL-divergence. Based on these facts, a co-clustering algorithm based on α-divergence is derived from an optimization problem built by extending the KL-divergence in conventional methods to α-divergence. This paper also demonstrates some numerical examples for the proposed methods.
本文言語 | English |
---|---|
ホスト出版物のタイトル | FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
巻 | 2015-November |
ISBN(電子版) | 9781467374286 |
DOI | |
出版ステータス | Published - 2015 11月 25 |
イベント | IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey 継続期間: 2015 8月 2 → 2015 8月 5 |
Other
Other | IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 |
---|---|
国/地域 | Turkey |
City | Istanbul |
Period | 15/8/2 → 15/8/5 |
ASJC Scopus subject areas
- ソフトウェア
- 理論的コンピュータサイエンス
- 人工知能
- 応用数学