Table 2 Steps of EM algorithm

Step1:

The number of categories K is preset as 3, then set the initial values of \(\theta\) for each component K and calculate the log-likelihood value in Eq. (7)

Step2: E step

Based on current values of \(\theta\), the value of z for each sample is estimated

Step3:M step

The values of z in Eq. (7) are updated, and the log-likelihood value is maximized to get a new set of \(\theta\) values

Step4:

Return to Step 2 until convergence