On March 9th, the seminar series was delighted to welcome Prof. Nicos Pavlidis, Senior Lecturer at Lancaster University to speak about new work developing spectral subspace clustering methods with extensions to constrained clustering and active learning.
Abstract: Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based subspace clustering algorithm that seeks to represent each point as a sparse convex combination of a few nearby points. We then extend the algorithm to constrained clustering and active learning. Our motivation for developing such a framework stems from the fact that
typically either a small amount of labelled data is available in advance; or it is possible to label some points at a cost. The latter scenario is typically encountered in the process of validating a cluster assignment.