  Multi-label classification is a common supervised machine learning problem
where each instance is associated with multiple classes. The key challenge in
this problem is learning the correlations between the classes. An additional
challenge arises when the labels of the training instances are provided by
noisy, heterogeneous crowdworkers with unknown qualities. We first assume
labels from a perfect source and propose a novel topic model where the present
as well as the absent classes generate the latent topics and hence the words.
We non-trivially extend our topic model to the scenario where the labels are
provided by noisy crowdworkers. Extensive experimentation on real world
datasets reveals the superior performance of the proposed model. The proposed
model learns the qualities of the annotators as well, even with minimal
training data.
