@@ -19,8 +19,8 @@ In summary, in addition to finding the the hyper-parameters $`\pi, \mu, \Sigma_{
### Pseudo code - Algorithm 1. EM for the Fusion of GMMs
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.
3. Initialise the transformation matrix $`\mathbf{U}`$ using Algorithm 3.
2. Initialise the means $`\mu_{k}^{L}`$, $`\mu_{k}^{H}`$, covariances $`\Sigma_{k}^{L}`$, $`\Sigma_{k}^{H}`$, and mixing coefficients $`\pi_k`$ using the K-means assignment $`R^{(0)}`$, and evaluate the initial likelihood.
3. Initialise the transformation matrix $`\mathbf{U} = \mathbf{MN}^{T}`$, where $`\mathbf{MDN}^{T}`$ is the SVD of $`\sum_{k=1}^{K}\mu_{k}^{H}(\mu_{k}^{L})^{T}`$.
4. For iteration = $`1, 2, ...`$, do
-**E-step.** Evaluate the responsibilities using the current parameter values