Title :
A machine learning enhanced empirical mode decomposition
Author :
Looney, D. ; Mandic, D.P.
Author_Institution :
Imperial Coll. London, London
fDate :
March 31 2008-April 4 2008
Abstract :
Empirical mode decomposition (EMD) is a fully data driven method for decomposing signals into a set of AM-FM components known as intrinsic mode functions (IMFs). Despite its usefulness in the analysis of real world signals, the process is rather deterministic and sensitive to parameters such as local envelope estimation. A combination of EMD and machine learning is proposed which provides an algorithm that is more robust to EMD parameters. In addition, the proposed extension is fully adaptive and facilitates the "data fusion via fission" mode of operation. The derivation and analysis of the proposed framework is supported with simulations in denoising and prediction applications.
Keywords :
learning (artificial intelligence); signal denoising; empirical mode decomposition; intrinsic mode functions; machine learning; Educational institutions; Electronic mail; Error correction; Inspection; Least squares approximation; Machine learning; Noise level; Noise reduction; Robustness; Signal restoration; adaptive filtering; empirical mode decomposition (EMD); feature fusion; machine learning;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518005
