Constraint projections for semi-supervised affinity propagation

Affinity propagation (AP) is introduced as an unsupervised learning algorithm for exemplar-based clustering. A few methods are stated to extend the AP model to account for semi-supervised clustering. In this paper, constraint (cannot-link and must-link) projections are illustrated for semi-supervised AP (CPSSAP), a hierarchical semi-supervised clustering algorithm. It is flexible for the relaxation of some constraints during the learning stage. First, the data points of instance-level constraints and other data points are together projected in a lower dimensional space guided by the constraints. Then, AP is performed on the new data points in the lower dimensional space. Finally, a few datasets are chosen for experimentation from the UCI machine learning repository. The results show that CPSSAP performs better than some existing algorithms. Furthermore, visualizations of the original data and data after the projections show that the data points overlap less after the constraint projections of the datasets.