Bayesian Independent Component Analysis with prior constraints: An application in biosignal analysis
In many data-driven machine learning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlyi...
| Main Authors: | , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2005
|
| Summary: | In many data-driven machine learning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlying latent sources may be different to the number of observations and hence issues of model complexity plague the analysis. Recent developments in Independent Component Analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, data decomposition may be achieved in a mathematically elegant manner. In this paper we extend the general ICA paradigm to include a very flexible source model and prior constraints and argue that for particular biomedical signal processing problems (we consider EEG analysis) we require the constraint of positivity in the mixing process. © Springer-Verlag Berlin Heidelberg 2005. |
|---|