A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule

This letter presents a Hebb-type learning algorithm for online linear calculation of principal components. The proposed method is based on a recently proposed cooperative-competitive concept, named the time-oriented hierarchical method. The algorithm performs deflation on the signal power rather than on the signal itself. It will be also shown when, or how, this algorithm can be used as a blind signal separation algorithm. The proposed synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. The number of necessary global calculation circuits is one.