If this project survived, I will post codes for the "non-experts" problem.
- Use PCA instead of linear algebra.
- Forget to normalize
$\theta$ at the beginning. However, normalized$\widehat{\theta}$ and$\widetilde\theta$ is estimated later. -
$\alpha^{(r)}$ should be different for each annotator. However, I forgot it and let the intercept be the same because the logistic regression by default has only one intercept.- However, at present I still failed to handle it beautifully. It seems that I should set some constraints on the intercept
$\alpha^{(r)}$ . My requirements are not support by standard logistic codes offered by sklearn. I should write my own codes.
- However, at present I still failed to handle it beautifully. It seems that I should set some constraints on the intercept
- The ultimate
$\widetilde\theta$ should has norm 1. However, after one-step update, it may not repect this restriction. Thus, a standardization is forced on$\widetilde\theta$ .