Functional Principal Component Analysis via Regularized Basis Expansion and Its Application

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  • 正則化基底展開法に基づく関数主成分分析とその応用
  • セイソクカ キテイ テンカイホウ ニ モトヅク カンスウ シュセイブン ブンセキ ト ソノ オウヨウ

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Abstract

Recently, functional data analysis (FDA) has received considerable attention in various fields and a number of successful applications have been reported (see, e.g., Ramsay and Silverman (2005)). The basic idea behind FDA is the expression of discrete observations in the form of a function and the drawing of information from a collection of functional data by applying concepts from multivariate data analysis.<BR>There are some reports discussing principal component analysis for functional data. We introduce the regularized functional principal component analysis for multi-dimensional functional data set, using Gaussian radial basis functions.<BR>The use of the proposed method is illustrated through the analysis of the three-dimensional (3D) protein structural data by converting the 3D protein data to the 3-dimensional functional data set. The visual inspection showed that the PC (principal component) plot mostly coincided with the biological classification.

Journal

  • Ouyou toukeigaku

    Ouyou toukeigaku 35 (1), 1-16, 2006

    Japanese Society of Applied Statistics

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