We propose a framework to learn the graph structure underlying a set of smooth signals.
Given X∈\mathbbR^ m\times n whose rows reside on the vertices of an unknown graph, we …

The construction of a meaningful graph plays a crucial role in the success of many graph-
based representations and algorithms for handling structured data, especially in the …

Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

Defining a sound shift operator for graph signals, similar to the shift operator in classical
signal processing, is a crucial problem in graph signal processing (GSP), since almost all …

Signal processing on graphs is an emerging research field dealing with signals living on an
irregular domain that is captured by a graph, and has been applied to sensor networks …

Cyber-physical systems have recently emerged in several practical engineering applications
where security and privacy are of paramount importance. This motivated the paper and a …

Dictionary learning (DL) techniques aim to find sparse signal representations that capture
prominent characteristics in a given data. Such methods operate on a data matrix Y∈ RN× …

Block-based compression scheme shows remarkable success in image and video coding.
However, existing tree-type block partition methods usually divide point clouds into clusters …

Due to the enormous potential of modelling, graph-based approaches have been used for
various applications in the process industries. In this study, we propose a fault detection …

Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements
that commonly require dimensionality reduction before being used for further analysis or …