The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Predicting Unknown Composition of a Mixture Using Independent Component Analysis

독립성분분석을 이용한 혼합물의 미지성분비율 예측

  • 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)
  • 이혜선 (경북대학교 통계학과) ;
  • 송재기 (경북대학교 통계학과) ;
  • 박해상 (포항공과대학교 산업경영공학과) ;
  • 전치혁 (포항공과대학교 산업경영공학과)
  • Published : 2006.03.01
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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.

독립성분분석은 차원이 높은 다변량데이타로부터 기저구조를 형성하는 독립성분을 분리하는데 사용되는 기법으로서 패턴인식, 예측 등 2차적 분석을 위한 1차 분석단계에서 사용할 수 있다. 본 연구에서는 독립성분분석을 이용하여 여러 혼합물 데이터로부터 독립성분을 분리한 다음 각 구성성분의 혼합비율을 예측하는 절차를 제안한다. 적용예로서 도금강판의 엑스선 회절강도값으로부터 여러가지 상을 분리한 다음 비음최소자승법을 이용하여 각 상의 분율을 예측하였으며, 이러한 제안방안의 타당성 평가를 위하여 모의 실험을 실시하였다.

Keywords

References

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