Paper No: 80
Speeding up FastICA by Mixture Random Pruning
Author(s): Giuliano Grossi, Sabrina Gaito
Abstract
We study and derive a method to speed up kurtosis-based FastICA in
presence of information redundancy, i.e., for large samples. It
consists in randomly decimating the data set as more as possible
while preserving the quality of the reconstructed signals. By
performing an analysis of the kurtosis estimator, we find the
maximum reduction rate which guarantees a narrow confidence
interval of such estimator with high confidence level. Such a
rate depends on a parameter $\beta$ easily computed a
priori combining together the fourth and the eighth norms of
the observations. Extensive simulations have been done on different sets
of real world signals. They show that actually the sample size reduction
is very high, preserves the quality of the decomposition and
impressively speeds up FastICA. On the other hand, the simulations
also show that, decimating data more than the rate fixed by
$\beta$, the decomposition ability of FastICA is compromised, thus
validating the reliability of the parameter $\beta$. We are
confident that our method will follow to better approach real time
applications. |
