Fast kernel density independent component analysis

Authors:
Aiyou Chen
Affiliations:
Bell Labs, Lucent Technologies
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

We develop a super-fast kernel density estimation algorithm (FastKDE) and based on this a fast kernel independent component analysis algorithm (KDICA). FastKDE calculates the kernel density estimator exactly and its computation only requires sorting n numbers plus roughly 2n evaluations of the exponential function, where n is the sample size. KDICA converges as quickly as parametric ICA algorithms such as FastICA. By comparing with state-of-the-art ICA algorithms, simulation studies show that KDICA is promising for practical usages due to its computational efficiency as well as statistical efficiency. Some statistical properties of KDICA are analyzed.