An Ensemble Learning Approach to Nonlinear Independent Component Analysis
Antti Honkela, Juha Karhunen · 2001
Abstract — Blind extraction of independent sources from their nonlinear mixtures is generally a very difficult problem. This is because both the nonlinear mapping and the underlying sources are unknown, and must be learned in an unsupervised manner from the data. We use multilayer perceptrons as nonlinear generative models for the data, and apply Bayesian ensemble learning for optimizing the model. In this paper, we successfully apply this approach to real-world speech data. 1