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Semi-supervised learning by DDD with a sharing base function

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

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  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

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

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