SIAM Journal on Scientific and Statistical Computing
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Stochastic blind equalization based on PDF fitting using Parzen estimator
IEEE Transactions on Signal Processing
An error-entropy minimization algorithm for supervised training ofnonlinear adaptive systems
IEEE Transactions on Signal Processing
Error-Entropy Minimization for Dynamical Systems Modeling
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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In this paper, we propose a fast and accurate approximation to the information potential of Information Theoretic Learning (ITL) using the Fast Gauss Transform (FGT). We exemplify here the case of the Minimum Error Entropy criterion to train adaptive systems. The FGT reduces the complexity of the estimation from O(N2) to O(pkN) wherep is the order of the Hermite approximation and k the number of clusters utilized in FGT. Further, we show that FGT converges to the actual entropy value rapidly with increasing order p unlike the Stochastic Information Gradient, the present O(pN) approximation to reduce the computational complexity in ITL. We test the performance of these FGT methods on System Identification with encouraging results.