id: 05721189
dt: j
an: 05721189
au: Diamantaras, Konstantinos I.; Papadimitriou, Theophilos
ti: Applying PCA neural models for the blind separation of signals.
so: Neurocomputing 73, No. 1-3, 3-9 (2009).
py: 2009
pu: Elsevier Science Publishers B.V., Amsterdam
la: EN
cc: 
ut: blind source separation; principal component analysis; PCA; oriented PCA;
    independent component analysis; ICA
ci: 
li: doi:10.1016/j.neucom.2008.05.018
ab: Summary: Principal component analysis is often thought of as a
    preprocessing step for blind source separation (BSS). Although second
    order methods have been proposed for BSS in the past, these approaches
    cannot be easily implemented by neural models. In this paper we
    demonstrate that PCA is more than a preprocessing step and, in fact, it
    can be used directly for solving the BSS problem in combination with
    very simple temporal filtering process. We also demonstrate that a PCA
    extension called oriented PCA (OPCA) can be also used for the same
    purpose without prewhitening the observed data. Both approaches can be
    implemented using efficient neural models that are shown to
    successfully extract the hidden sources.
rv: