@inproceedings{311f248e110944a4a09d6c0d5a55c3ff,
title = "A recursive online kernel PCA algorithm",
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 = "Kernel methods, Online learning, PCA",
author = "Erion Hasanbelliu and Giraldo, \{Luis S{\'a}nchez\} and Principe, \{Jos{\'e} C.\}",
year = "2010",
doi = "10.1109/ICPR.2010.50",
language = "English",
isbn = "9780769541099",
series = "Proceedings - International Conference on Pattern Recognition",
pages = "169--172",
booktitle = "Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010",
note = "2010 20th International Conference on Pattern Recognition, ICPR 2010 ; Conference date: 23-08-2010 Through 26-08-2010",
}