Unsupervised learning of higher-order statistics
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.
unsupervised learning tensors high-order statistics adaptive signal processing blind signal processing