Commit e50d2f75 authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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parent eb19c85e
## Fusion of high-quality and low-quality data - GMM-based implementation
<img src="/figs/2021JUL21/diagram-20210721.png" width="800">
### Model formulation
Suppose $`\mathbf{X}^{H}, \mathbf{X}^{L}`$ are $`N \times V`$ feature matrices (e.g. connectivity between $`N`$ thalamus voxels and $`V`$ whole brain voxels). Note that they can have different dimensions in practice. To keep notations uncluttered, we suppose the number of voxels in high- and low-quality images are the same for a given subject. Now we assume $`\mathbf{X}^{H}, \mathbf{X}^{L}`$ share the same latent variable $`Y`$, which is a $`N \times K`$ binary matrix representing the voxels' classes.
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