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http://dx.doi.org/10.5351/KJAS.2006.19.1.135

Predicting Unknown Composition of a Mixture Using Independent Component Analysis  

Lee Hye-Seon (Department of Statistics, Kyungpook National University)
Song Jae-Kee (Department of Statistics, Kyungpook National University)
Park Hae-Sang (Department of Industrial & Management Engineering, POSTECH)
Jun Chi-Hyuck (Department of Industrial & Management Engineering, POSTECH)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.1, 2006 , pp. 135-148 More about this Journal
Abstract
Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.
Keywords
Independent component analysis; Non-negative least square; non-Gaussian;
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  • Reference
1 Lee, D. D. and Seung, H. S. (1999). 'Learning the parts of objects by non-negative matrix factorization', Nature, vol. 401, 788-791   DOI   ScienceOn
2 Hyvarinen, A. (1999). 'Survey on independent component analysis', Neural Computing Surveys, 2, 94-128
3 Hyvarinen, A. and Oja, E. (2000). 'Independent component analysis: algorithms and applications', Neural Networks, 13(4-5), 411-430   DOI   ScienceOn
4 Ikeda, S. and Toyama, K. (2000). 'Independent component analysis for noisy data-MEG data analysis', Neural Networks 13, 1063-1074   DOI   ScienceOn
5 Lawson, C. L. and Hanson, R. J. (1974). Solving least squares problems, Prentice-Hall, Englewood Cliffs, NJ, Chapter 23
6 Lee, D. D. and Seung, H. S. (2001). 'Algorithms for nonnegative matrix factorization,' in Advances in Neural Information Processing Systems 13. Cambridge, MA: MIT Press, 556-562
7 Vigario, R., Jousmaki, V., Hyvarinen, M., Hari, R. and Oja, E. (1998). Independent component analysis for identification of artifacts in magnetoencephalographic recordings, Advances in Neural Information Processing Systems, Vol. 10. Cambridge, MA:MIT Press (pp. 229-235)