1 research outputs found
Bayesian Semi-supervised learning under nonparanormality
Semi-supervised learning is a classification method which makes use of both
labeled data and unlabeled data for training. In this paper, we propose a
semi-supervised learning algorithm using a Bayesian semi-supervised model. We
make a general assumption that the observations will follow two multivariate
normal distributions depending on their true labels after the same unknown
transformation. We use B-splines to put a prior on the transformation function
for each component. To use unlabeled data in a semi-supervised setting, we
assume the labels are missing at random. The posterior distributions can then
be described using our assumptions, which we compute by the Gibbs sampling
technique. The proposed method is then compared with several other available
methods through an extensive simulation study. Finally we apply the proposed
method in real data contexts for diagnosing breast cancer and classify radar
returns. We conclude that the proposed method has better prediction accuracy in
a wide variety of cases