Fast iterative kernel principal component analysis
We develop gain adaptation methods that improve convergence of the kernel Hebbian algorithm (KHA) for iterative kernel PCA (Kim et al., 2005). KHA has a scalar gain parameter which is either held constant or decreased according to a predetermined annealing schedule, leading to slow convergence. We accelerate it by incorporating the reciprocal of the current estimated eigenvalues as part of a gain vector. An additional normalization term then allows us to eliminate a tuning parameter in the...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Guenter_Fast2007.pdf||3.11 MB||Adobe PDF|
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