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.
Original language | English |
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Pages | 317-320 |
Publication status | Published - 2002 |
Event | 11th European Signal Processing Conference EUSIPCO'2002 - Toulouse, France Duration: 3 Sept 2002 → 6 Sept 2002 |
Conference
Conference | 11th European Signal Processing Conference EUSIPCO'2002 |
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Country/Territory | France |
City | Toulouse |
Period | 3/09/02 → 6/09/02 |
Keywords
- de-noising
- non-linear signals
- principal component analysis
- kernel functions