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 .
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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),
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
Note: the “accuracy” is different from “AUR”.