Signal Separation by Nonlinear Hebbian Learning
Erkki Oja, Juha Karhunen · 1995
this paper, we introduce a neural network that can be used for both source separation and the estimation of the basis vectors of ICA. The remainder of the paper is organized as follows. The next section presents the necessary background on ICA and source separation. In the third section, we introduce and justify the basic neural network learning algorithms for signal separation. The fourth section provides mathematical analysis justifying the separation ability of the nonlinear PCA type learning algorithm. The fifth section then introduces the ICA neural network, a three-layer network whose layers perform input data whitening, separation, and ICA basis vector estimation, respectively. In the sixth section, we present experimental results. In the last section, the conclusions of this study are presented, and some possibilities for extending the data model are outlined.