Kernel Principal Component Analysis (KPCA) for the de-noising of communication signals
Page numbers in the proceedings:
Volume I pp 317-320
Nonlinear Signal and Systems / Adaptive Methods
This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a linear signal in a higher dimensional space. In that feature space, a linear algorithm can be applied to a non-linear problem. It is proposed that using the principal components extracted from this feature space, the signal can be de-noised in its input space.
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