Koutsogiannis, G. and Soraghan, J.J. (2002) Kernel principal component analysis (KPCA) for the de-noising of communication signals. In: 11th European Signal Processing Conference EUSIPCO'2002, 2002-09-03 - 2002-09-06.
paper049.pdf - Final Published Version
Download (288kB) | Preview
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.
|Item type:||Conference or Workshop Item (Paper)|
|Keywords:||de-noising, non-linear signals, principal component analysis , kernel functions, Electrical engineering. Electronics Nuclear engineering|
|Subjects:||Technology > Electrical engineering. Electronics Nuclear engineering|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering|
|Depositing user:||Pure Administrator|
|Date Deposited:||23 Apr 2012 12:05|
|Last modified:||22 Nov 2016 21:25|