書誌事項
- タイトル別名
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- Functional Principal Component Analysis via Regularized Basis Expansion and Its Application
- 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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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.
収録刊行物
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- 応用統計学
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応用統計学 35 (1), 1-16, 2006
応用統計学会
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詳細情報 詳細情報について
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- CRID
- 1390282679418466816
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- NII論文ID
- 10018231100
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- NII書誌ID
- AN00330942
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- ISSN
- 18838081
- 02850370
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- NDL書誌ID
- 8054698
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可
