Principal Component Analysis for Data with Tolerance

• Tsuji Tatsuyoshi

  University of Tsukuba

• Endo Yasunori

  University of Tsukuba

• Hamasuna Yukihiro

  Kinki University

• Kurihara Kouta

  University of Tsukuba

Bibliographic Information

Other Title

• 許容範囲付きデータに対する主成分分析

Abstract

In general, data contain the uncertainty of the error, range, and the loss, etc, and thus, the data are handled as intervals on the pattern space. The concept of tolerance in this paper enables these data to be handled as a point on the pattern space by using tolerance vectors. The advantage is that we can handle uncertain data in the framework of optimization without introducing any particular measures between intervals. In recent years, this concept is positively introduced into clustering methods and the effectiveness is confirmed. However, we can not find the application of the concept into multivariate analysis methods except regression models in spite of its effectiveness. Therefore, in this paper, we propose a new algorithm of principal component analysis for uncertain data by introducing the concept of the tolerance. Moreover, we verify the effectiveness through some numerical examples.

Journal

• Proceedings of the Fuzzy System Symposium

  Proceedings of the Fuzzy System Symposium 27 (0), 76-76, 2011

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Principal Component Analysis
• uncertain data
• tolerance

Details

• CRID: 1390001205673954688
• NII Article ID: 130004591989
• DOI: 10.14864/fss.27.0.76.0
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed