Joint graph learning and blind separation of smooth graph signals using minimization of mutual information and Laplacian quadratic forms
A Einizade, SH Sardouie - IEEE Transactions on Signal and …, 2023 - ieeexplore.ieee.org
The smoothness of graph signals has found desirable real applications for processing
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …
Graph signal separation based on smoothness or sparsity in the frequency domain
S Mohammadi, M Babaie-Zadeh… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this paper, we study the problem of demixing an observed signal, which is the summation
of a set of signals that live on a multi-layer graph, by proposing several methods to …
of a set of signals that live on a multi-layer graph, by proposing several methods to …
A new approach for graph signal separation based on smoothness
MHA Yarandi, M Babaie-Zadeh - IEEE Transactions on Signal …, 2024 - ieeexplore.ieee.org
Blind source separation (BSS) is a signal processing subject that has recently been
extended to graph signals. Graph signals that are smooth on their own graphs provide an …
extended to graph signals. Graph signals that are smooth on their own graphs provide an …
Estimation of a causal directed acyclic graph process using non-gaussianity
In machine learning and data mining, causal relationship discovery is a critical task. While
the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR …
the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR …