Update 2021JUL21.md

2021JUL21.md

@@ -19,7 +19,6 @@ In summary, in addition to finding the the hyper-parameters $`\pi, \mu, \Sigma_{
 
 ### Pseudo code
 Algorithm 1. EM for the Fusion of GMMs
----
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  1. Run K-means clustering on the high-quality data to generate the assignment of the voxels $`R^{(0)}`$.
  2. Initialise the means $`\mu_{k}`$, covariances $`\Sigma_{k}`$, and mixing coefficients $`\pi_k`$ using the K-means assignment $`R^{(0)}`$, and evaluate the initial likelihood.
@@ -33,6 +32,8 @@ Algorithm 1. EM for the Fusion of GMMs
        - $`\Sigma_{k}^{H} = \frac{1}{N_{k}}\sum_{n=1}^{N}\gamma(y_{nk})(\mathbf{Ux}^{H}_{n} - \mathbf{\mu}_{k})(\mathbf{Ux}^{H}_{n} - \mathbf{\mu}_{k})^{T}`$
        - $`\pi_k = \frac{N_{k}}{N}`$
        - $`\mathbf{U}=`$ 
+    - Evaluate the likelihood and check for convergence.
+ 5. Using $`\mu_{k}, \Sigma_{k}^{L}, \pi_{k}`$ to assignment unseen data.
 
 ### Simulation results
 #### We considered three scenarios