TY - GEN
T1 - A recursive online kernel PCA algorithm
AU - Hasanbelliu, Erion
AU - Giraldo, Luis Sánchez
AU - Principe, José C.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Kernel methods
KW - Online learning
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=78149473308&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149473308&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.50
DO - 10.1109/ICPR.2010.50
M3 - Conference contribution
AN - SCOPUS:78149473308
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 169
EP - 172
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -