In this paper we introduce two new methods for solving binary quadratic problems. While spectral relaxation methods have been the workhorse subroutine for a wide variety of comput...
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
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), ...
We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, ...
Linli Xu, James Neufeld, Bryce Larson, Dale Schuur...