A CCA Criterion Based Adaptive Algorithm for Blind Extraction of Specific Signal
Keywords:
Blind source separation, canonical correlation analysis, steepest descent, Newton iteration.Abstract
In this paper, the canonical correlation approach
for blind source separation is revisited. We proposed a novel
criterion for source extraction, which is proved to be equivalent
to the CCA criterion, whereas it has some advantages over the
standard CCA criterion in developing fast learning algorithms.
By optimizing the proposed criterion, we developed two
learning algorithms for the extraction of specific signals. The
first algorithm is based on the steepest descent technique, and
the second one is a modified Newton algorithm. The experiment
results demonstrate the effectiveness of the proposed learning
algorithms, and show that the modified Newton algorithm
converges much faster than the other extraction method.
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