TY - GEN
T1 - A study of increasing the speed of the Independent Component Analysis (ICA) using wavelet technique
AU - Usman, Koredianto
AU - Juzoji, Hiroshi
AU - Nakajima, Isao
AU - Sadiq, Muhammad Athar
PY - 2004
Y1 - 2004
N2 - Independent Component Analysis (ICA) is a multivariate data analysis tool. The basic principle of ICA is the assumption of independency of the source data. On the separation of the data source, ICA algorithm searches for a demixing matrix that will maximize the independency. This searching process is mostly done in iterative way and involving high order statistics. This process is time consuming. For a certain application, such as speech, where the source signal has its power at the lower frequency, we can reduce the data length by removing the high frequency component. Wavelet decomposition is a popular method for this purpose. In this paper, we propose the data reduction using Wavelet as a preprocessing of ICA to speed up the ICA computation. We investigate Haar, Daubechies 2, Daubechies 3, and Daubechies 4 Wavelet as the wavelet analysis. We further investigate the computation time as the function of level of decomposition of the wavelet. In this study, we found that Haar Wavelet at third level of decomposition gave the biggest advantage of computation speed, which is about 40-50%.
AB - Independent Component Analysis (ICA) is a multivariate data analysis tool. The basic principle of ICA is the assumption of independency of the source data. On the separation of the data source, ICA algorithm searches for a demixing matrix that will maximize the independency. This searching process is mostly done in iterative way and involving high order statistics. This process is time consuming. For a certain application, such as speech, where the source signal has its power at the lower frequency, we can reduce the data length by removing the high frequency component. Wavelet decomposition is a popular method for this purpose. In this paper, we propose the data reduction using Wavelet as a preprocessing of ICA to speed up the ICA computation. We investigate Haar, Daubechies 2, Daubechies 3, and Daubechies 4 Wavelet as the wavelet analysis. We further investigate the computation time as the function of level of decomposition of the wavelet. In this study, we found that Haar Wavelet at third level of decomposition gave the biggest advantage of computation speed, which is about 40-50%.
KW - Decomposition
KW - Independent Component Analysis
KW - Low Frequency signals
KW - Wavelet
UR - http://www.scopus.com/inward/record.url?scp=14544285512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=14544285512&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:14544285512
SN - 0780384539
T3 - Proceedings - 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry, Healthcom 2004
SP - 73
EP - 75
BT - Proceedings - 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry, Healthcom 2004
A2 - Kurokawa, K.
A2 - Nakajima, I.
A2 - Ishibashi, Y.
T2 - Proceedings - 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry, Healthcom 2004
Y2 - 28 June 2004 through 29 June 2004
ER -