Semi-supervised learning by DDD with a sharing base function

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Semi-supervised learning by DDD with a sharing base function

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Semi-supervised learning by DDD with a sharing base function

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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),

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”.