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
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;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761298
