CURVE FITTlNG METHODS FOR DATA WITHOUT EXTERNAL CRITERION

• Mizuta Masahiro

  Information Initiative Center, Hokkaido University

Bibliographic Information

Other Title

• 外的基準のないデータに対する曲線当てはめについて
• ガイテキ キジュン ノ ナイ データ ニ タイスル キョクセン アテハメ ニ ツイテ

Abstract

In this paper, we survey methods for curve fitting for multidimensional data without external criterion. Curve fitting is a fundamental techunique for data analysis. Principal Components Analysis find out a line or linear subspace fitted for the data. But, it is not easy to get nonlinear curve fitting. We introduce three methods for nonlinear curve fitting; Generalized Principal Components Analysis, Algebraic Curve Fitting, and Principal Curve.

Journal

• Bulletin of the Computational Statistics of Japan

  Bulletin of the Computational Statistics of Japan 15 (2), 263-271, 2003

  Japanese Society of Computational Statistics

Keywords

• Algebraic curve fitting
• Principal curves
• Generalized principal components analysis
• Nonlinear data analysis

Details

• CRID: 1390282679361018368
• NII Article ID: 110001235518
• NII Book ID: AN10195854
• DOI: 10.20551/jscswabun.15.2_263
• ISSN: 21899789
• ISSN: 09148930
• NDL BIB ID: 7014963
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7014963
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed