In video based face recognition, great success has been made by representing videos as
linear subspaces, which typically lie in a special type of non-Euclidean space known as …

The extension of conventional clustering to hypergraph clustering, which involves higher
order similarities instead of pairwise similarities, is increasingly gaining attention in …

This paper deals with the extraction of multiple models from noisy or outlier-contaminated
data. We cast the multi-model fitting problem in terms of set covering, deriving a simple and …

Spectral graph partitioning methods have received significant attention from both
practitioners and theorists in computer science. Some notable studies have been carried out …

Spectral clustering is a celebrated algorithm that partitions the objects based on pairwise
similarity information. While this approach has been successfully applied to a variety of …

Geometric model fitting is a challenging but fundamental computer vision problem. Recently,
quantum optimization has been shown to enhance robust fitting for the case of a single …

This paper deals with the extraction of multiple models from outlier-contaminated data. The
method we present is based on preference analysis and low rank approximation. After …

In this paper, we propose a simple and effective geometric model fitting method to fit and
segment multi-structure data even in the presence of severe outliers. We cast the task of …

In a series of recent works, we have generalised the consistency results in the stochastic
block model literature to the case of uniform and non-uniform hypergraphs. The present …

Matrix spectral methods play an important role in statistics and machine learning, and most
often the word 'matrix'is dropped as, by default, one assumes that similarities or affinities are …