DocumentCode
2479048
Title
Adaptive Laplacian eigenfunctions as bases for regression analysis
Author
Ding, Lei ; Bai, Xiaole
Author_Institution
Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH
fYear
2008
fDate
8-11 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
Regression or least squares fitting is an important problem in statistics, data mining and many other applications. In recent years, basis functions derived from the underlying geometry of data, primarily Laplacian eigenfunctions, have attracted much interest. In this paper, we present a new framework based on adaptive Laplacian eigenfunctions and show the benefit of using a time-varying basis in regression analysis.
Keywords
Laplace equations; curve fitting; differential geometry; eigenvalues and eigenfunctions; least mean squares methods; regression analysis; adaptive Laplacian eigenfunction; data geometry; differential geometry; least squares fitting; regression analysis; time-varying basis; Application software; Computer science; Data engineering; Data mining; Eigenvalues and eigenfunctions; Geometry; Laplace equations; Least squares methods; Regression analysis; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location
Tampa, FL
ISSN
1051-4651
Print_ISBN
978-1-4244-2174-9
Electronic_ISBN
1051-4651
Type
conf
DOI
10.1109/ICPR.2008.4761298
Filename
4761298
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