Selecting inputs for modeling using normalized higher order statistics and independent component analysis
Andrew D. Back, Thomas P. Trappenberg · IEEE Transactions on Neural Networks · 2001
The problem of input variable selection is well known in the task of modeling real-world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches when the inputs are dependent.