… an extension of Oja's learning algorithm .for principal component analysis which is capable
of adapting the weights of a higher order neuron to pick up higher order correlations from a …

… In this paper, we have introduced a model that can learn higher order correlations within
the input data without suffering from the combinatorial explosion problem. This was made …

… Numerical experiments show that neural networks do indeed learn to … extract information
from higher-order correlations in the … and random features to learn from higher-order cumulants. …

… for learning the edge scores for correlation clustering [11]. However, his learning criterion is
… Even though the previous (pairwise) correlation clustering can consider global aspects of …

… order inputs correlations seen by each neuron within the pool. We determine how higher-order
correlations … We show how input nonlinearities can shape higher-order correlations and …

… In order to ascertain to what degree the stimulus driven component of activity accounts for
the learned higher-order correlations, we augmented the above models with a dynamic bias …

… memory with higher order learning rule is … The higher-order correlation model shows
dramatic improvement in its storage capacity in comparison to the conventional binary correlation …

… for unsupervised learning from higher-order correlations, especially when a neuron has
many inputs. It is well known that a normalized nonlinear Hebbian rule can learn “unmixing” …

… A high-order correlation tensor formalism for neural networks is described. The model can …
like correlation matrix methods. The possibility of using multiassociative memory for a learning …

… learning … learning algorithm that whether a higherorder term is to be taken into consideration
or not. Some approaches, in which learning complexity is decided by the number of higher-…