Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

The effective representation, processing, analysis, and visualization of large-scale structured
data, especially those related to complex domains, such as networks and graphs, are one of …

In this work, we are interested in generalizing convolutional neural networks (CNNs) from
low-dimensional regular grids, where image, video and speech are represented, to high …

Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete
views to improve clustering performance. Among various excellent solutions, multiple kernel …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we
propose a randomized resampling strategy that selects a representative subset of points …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

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 …

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 …