한국데이터정보과학회:학술대회논문집
- 한국데이터정보과학회 2005년도 춘계학술대회
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- Pages.127-134
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- 2005
Predicting Unknown Composition of a Mixture Using Independent Component Analysis
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Lee, Hye-Seon
(Department of Industrial and Management Engineering, POSTECH) ;
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Park, Hae-Sang
(Department of Industrial and Management Engineering, POSTECH) ;
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Jun, Chi-Hyuck
(Department of Industrial and Management Engineering, POSTECH)
- 발행 : 2005.04.29
초록
A suitable representation for the conceptual simplicity of the data in statistics and signal processing is essential for a subsequent analysis such as prediction, pattern recognition, and spatial analysis. 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.