Commit 472e5b46 authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
Browse files


parent edafd11f
......@@ -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
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
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