Title :
A Recursive Online Kernel PCA Algorithm
Author :
Hasanbelliu, Erion ; Giraldo, Luis Sánchez ; Principe, José C.
Author_Institution :
ECE Dept., Univ. of Florida, Gainesville, FL, USA
Abstract :
In this paper, we describe a new method for performing kernel principal component analysis which is online and also has a fast convergence rate. The method follows the Rayleigh quotient to obtain a fixed point update rule to extract the leading eigenvalue and eigenvector. Online deflation is used to estimate the remaining components. These operations are performed in reproducing kernel Hilbert space (RKHS) with linear order memory and computation complexity. The derivation of the method and several applications are presented.
Keywords :
Hilbert spaces; computational complexity; eigenvalues and eigenfunctions; feature extraction; principal component analysis; RKHS; Rayleigh quotient; computation complexity; convergence rate; eigenvalue; eigenvector; fixed point update rule; kernel principal component analysis; linear order memory; online deflation; recursive online kernel PCA algorithm; reproducing kernel Hilbert space; Complexity theory; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Image reconstruction; Kernel; Principal component analysis; Kernel Methods; Online learning; PCA;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.50
