가우시안 혼합 모델에 대한 EM 알고리즘을 이용한 신호와 잡음의 분리

Separating Signals and Noises Using EM Algorithm for Gaussian Mixture Model

For the quantitative analysis of inclusion using OES data, separating of noise and inclusion is needed. In previous methods assuming that noises come from a normal distribution, intensity levels beyond a specific threshold are determined as inclusions. However, it is not possible to classify inclusions in low intensity region using this method, even though every inclusion is an element of some chemical compound. In this paper, we assume that distribution of OES data is a Gaussian mixture and estimate the parameters of the mixture model using EM algorithm. Then, we calculate mixing ratio of noise and inclusion using these parameters to separate noise and inclusion.