Title :
Supervised-unsupervised combined neural learning for independent component analysis
Author :
Chen, Yang ; He, Zhenya
Author_Institution :
Dept. of Radio Eng., Southeast Univ., Nanjing, China
Abstract :
A neural network approach to independent component analysis (ICA) is proposed. The supervised-learning backpropagation rule is used to train multilayer perceptron for approximating the signal distribution adaptively, giving an appropriate estimate of the nonlinear activation function in the unsupervised learning rule. A comparison with purely unsupervised learning is also made.
Keywords :
backpropagation; function approximation; independent component analysis; multilayer perceptrons; unsupervised learning; backpropagation rule; independent component analysis; multilayer perceptron; neural network; nonlinear activation function; supervised-learning; unsupervised learning rule; Biomedical signal processing; Distribution functions; Electronic mail; Helium; Independent component analysis; Multilayer perceptrons; Neural networks; Source separation; Supervised learning; Unsupervised learning;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1202845
