Functional Principal Component Analysis via Regularized Basis Expansion and Its Application
-
- Kayano Mitsunori
- Graduate School of Mathematics, Kyushu University
-
- Konishi Sadanori
- Faculty of Mathematics, Kyushu University
-
- Hirakawa Hideki
- Faculty of Agriculture, Kyushu University
-
- Kuhara Satoru
- Faculty of Agriculture, Kyushu University
Bibliographic Information
- Other Title
-
- 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
Search this article
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
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390282679418466816
-
- NII Article ID
- 10018231100
-
- NII Book ID
- AN00330942
-
- ISSN
- 18838081
- 02850370
-
- NDL BIB ID
- 8054698
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed