Paper data
Title:
Kernel Principal Component Analysis (KPCA) for the de-noising of communication signals Author(s): Koutsogiannis Grigorios, Soraghan John, Page numbers in the proceedings: Volume I pp 317-320 Session: Nonlinear Signal and Systems / Adaptive Methods
Paper abstract
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.
Paper
|