Self-taught spectral clustering via constraint augmentation

Abstract

Although constrained spectral clustering has been used extensively for the past few years, all work assumes the guidance (constraints) are given by humans. Original formulations of the problem assumed the constraints are given passively whilst later work allowed actively polling an Oracle (human experts). In this paper, for the first time to our knowledge, we explore the problem of augmenting the given constraint set for constrained spectral clustering algorithms. This moves spectral clustering towards the direction of self-teaching as has occurred in the supervised learning literature. We present a formulation for self-taught spectral clustering and show that the self-teaching process can drastically improve performance without further human guidance.