Nonlinear source separation using ensemble learning and MLP networks
Hanna K. Lappalainen, Antti Honkela, Xavier Giannakopoulos, Juha Karhunen · 2002
We consider extraction of independent sources from their nonlinear mixtures. Generally, this problem is very difficult, because both the nonlinear mapping and the underlying sources are unknown and should be learned from the data. We use multilayer perceptrons as nonlinear generative models for the data. The model indeterminacy problem is resolved by applying ensemble learning. This Bayesian method selects the most probable generative data model. In simulations with artificial data, the network is able to find the underlying sources from the observations only, even though the data generating mapping is strongly nonlinear. We have applied the developed method also to real-world process data.