Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

Abstract

We propose a new weakly supervised approach for classification and clustering based on mixture models. Our
approach integrates multi-level pairwise group and class
constraints between samples to learn the underlying
group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the
number of classes is known but does not assume any
prior knowledge about the number of mixture components in each class. Therefore, our model : (1) allocates
multiple mixture components to individual classes, (2)
estimates automatically the number of components of
each class, 3) propagates class labels to unlabelled data
in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets
show the robustness and performance of our model over
state-of-the-art methods.