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 …

Recently, 3D visual representation models such as light fields and point clouds are
becoming popular due to their capability to represent the real world in a more complete and …

Graph signal processing models high dimensional data as functions on the vertices of a
graph. This theory is constructed upon the interpretation of the eigenvectors of the Laplacian …

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 …

We consider the problem of inferring the conditional independence graph (CIG) of high-
dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph …

In image compression, classical block-based separable transforms tend to be inefficient
when image blocks contain arbitrarily shaped discontinuities. For this reason, transforms …

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 …