We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigendecomp...
Many existing spectral clustering algorithms share a conventional graph partitioning criterion: normalized cuts (NC). However, one problem with NC is that it poorly captures the g...
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...
Clustering performance can often be greatly improved by
leveraging side information. In this paper, we consider constrained
clustering with pairwise constraints, which specify
s...
We propose a novel framework for constrained spectral
clustering with pairwise constraints which specify whether
two objects belong to the same cluster or not. Unlike previous
m...
Zhenguo Li (The Chinese University of Hong Kong), ...