1. Semi-supervised learning by DDD with a sharing base function
- preliminary result on WDBC data

2. From the DDD formula
Consider all the Dirichlet distribution share a common base function (similar to what Dunson did),
Where
Affinity matrix
We choose

3. Semi-supervised learning (transductive way)
For those data , this is the conditional posterior for .
By performing MCMC, we can get the histograms of their full posteriors given the labeled data set.

4. Apply the DDD to one benchmark data set – WDBC
569 data with dimensionality 32
Randomly choose a portion of data as labeled data and calculate the area under ROC (AUR) for each trial.
The number of labeled data: [20, 40, 60, 80].
For each case: 20 random trials.
MCMC iteration: 2000; Burn-in: 500

6. Qiuhua’s results
Note: the “accuracy” is different from “AUR”.