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JACIII Vol.14 No.4 pp. 402-407
doi: 10.20965/jaciii.2010.p0402
(2010)

Paper:

Learning Similarity Matrix from Constraints of Relational Neighbors

Masayuki Okabe* and Seiji Yamada**

*Information and Media Center, Toyohashi University of Technology, 1-1 Tenpaku, Toyohashi, Aichi 441-8580, Japan

**National Institute of Informatics, the Graduate University for Advanced Studies (SOKENDAI), 2-1-2 Hitotsubashi, Chiyoda, Tokyko 101-8430, Japan

Received:
December 15, 2009
Accepted:
March 8, 2010
Published:
May 20, 2010
Keywords:
learning similarity matrix, semidefinite programming, constrained clustering
Abstract

This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matrix is learned by solving an optimization problem formalized as semidefinite programming. Additional constraints are used as complementary in the optimization problem. Results of experiments confirmed the effectiveness of our proposed method in several clustering tasks and that our method is a promising approach.

Cite this article as:
Masayuki Okabe and Seiji Yamada, “Learning Similarity Matrix from Constraints of Relational Neighbors,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.4, pp. 402-407, 2010.
Data files:
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Last updated on Jun. 08, 2021