Nonnegative Spectral Clustering for Large-Scale Semi-supervised Learning

Abstract

This paper proposes a novel clustering approach called Scalable Nonnegative Spectral Clustering (SNSC). Specifically, SNSC preserves the original nonnegative characteristic of the indicator matrix, which leads to a more tractable optimization problem with an accurate solution. Due to the nonnegativity, SNSC offers high interpretability to the indicator matrix, that is, the final cluster labels can be directly obtained without post-processing. SNSC also scales linearly with the data size, thus it can be easily applied to large-scale problems. In addition, limited label information can be naturally incorporated into SNSC for improving clustering performance. Extensive experiments demonstrate the superiority of SNSC as compared to the state-of-the-art methods.