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

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

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.

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

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