Unsupervised Learning of Higher-Order Statistics

摘要

This paper deals with the adaptive extraction of higher-order statistics of related signals. We will show how to use higher-order neural networks to adaptively extract the higher-order cumulant matrices and tensors with an invariant weight norm. This proposed scheme can serve as an alternative tool in many application fields with higher-order statistics.