Non-differentiable and constrained optimization play a key role in machine learning, signal
and image processing, communications, and beyond. For high-dimensional minimization …

Several models have been proposed to cope with the rapidly increasing size of data, such
as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph …

Hypergraphs allow to encode higher-order relationships in data and are thus a very flexible
modeling tool. Current learning methods are either based on approximations of the …

The analysis of signals defined over a graph is relevant in many applications, such as social
and economic networks, big data or biological networks, and so on. A key tool for analyzing …

This paper establishes the consistency of spectral approaches to data clustering. We
consider clustering of point clouds obtained as samples of a ground-truth measure. A graph …

We consider point clouds obtained as random samples of a measure on a Euclidean
domain. A graph representing the point cloud is obtained by assigning weights to edges …

We present two graph-based algorithms for multiclass segmentation of high-dimensional
data on graphs. The algorithms use a diffuse interface model based on the Ginzburg …

In this chapter we discuss the spectral theory of discrete structures such as graphs, simplicial
complexes and hypergraphs. We focus, in particular, on the corresponding Laplace …

This paper establishes the consistency of a family of graph-cut-based algorithms for
clustering of data clouds. We consider point clouds obtained as samples of a ground-truth …

Given a task T, a set of experts V with multiple skills and a social network G (V, W) reflecting
the compatibility among the experts, team formation is the problem of identifying a team C …