Graphs are fundamental mathematical structures used in various fields to represent data,
signals, and processes. In this paper, we propose a novel framework for learning/estimating …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

This paper introduces a novel graph signal processing framework for building graph-based
models from classes of filtered signals. In our framework, graph-based modeling is …

Recent papers have formulated the problem of learning graphs from data as an inverse
covariance estimation problem with graph Laplacian constraints. While such problems are …

In many state-of-the-art compression systems, signal transformation is an integral part of the
encoding and decoding process, where transforms provide compact representations for the …

This paper studies the problem of learning the large-scale Gaussian graphical models that
are multivariate totally positive of order two ($\text {MTP} _2 $). By introducing the concept of …

In this paper, we propose a new graph-based transform and illustrate its potential
application to signal compression. Our approach relies on the careful design of a graph that …

Graphs are fundamental mathematical structures used in various fields to represent data,
signals and processes. In this paper, we propose a novel framework for learning/estimating …

An unfocused plenoptic light field (LF) camera places an array of microlenses in front of an
image sensor in order to separately capture different directional rays arriving at an image …

Machine learning (ML) has been producing major advances in several technological fields
and can have a significant impact on media coding. However, fast progress can only happen …