Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

In many machine learning applications, data are coming with multiple graphs, which is
known as the multiple graph learning problem. The problem of multiple graph learning is to …

Deep learning has achieved a remarkable performance breakthrough in several fields, most
notably in speech recognition, natural language processing, and computer vision. In …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Graphs are a central tool in machine learning and information processing as they allow to
conveniently capture the structure of complex datasets. In this context, it is of high …

Can one reduce the size of a graph without significantly altering its basic properties? The
graph reduction problem is hereby approached from the perspective of restricted spectral …

An emerging way to deal with high-dimensional noneuclidean data is to assume that the
underlying structure can be captured by a graph. Recently, ideas have begun to emerge …

We study the problem of sampling k-bandlimited signals on graphs. We propose two
sampling strategies that consist in selecting a small subset of nodes at random. The first …

Graphs are a prevalent tool in data science, as they model the inherent structure of the data.
They have been used successfully in unsupervised and semi-supervised learning. Typically …

To support high-level analysis of spaceborne imaging spectroscopy (hyperspectral)
imagery, spectral unmixing has been gaining significance in recent years. However, from the …