A unified framework for structured graph learning via spectral constraints
Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …
the literature. Learning a structured graph is essential for interpretability and identification of …
Structured graph learning via Laplacian spectral constraints
Learning a graph with a specific structure is essential for interpretability and identification of
the relationships among data. But structured graph learning from observed samples is an …
the relationships among data. But structured graph learning from observed samples is an …
Fast randomized algorithms for t-product based tensor operations and decompositions with applications to imaging data
Tensors of order three or higher have found applications in diverse fields, including image
and signal processing, data mining, biomedical engineering, and link analysis, to name a …
and signal processing, data mining, biomedical engineering, and link analysis, to name a …
Learning Laplacian matrix from graph signals with sparse spectral representation
In this paper, we consider the problem of learning a graph structure from multivariate
signals, known as graph signals. Such signals are multivariate observations carrying …
signals, known as graph signals. Such signals are multivariate observations carrying …
Learning graphical factor models with riemannian optimization
Graphical models and factor analysis are well-established tools in multivariate statistics.
While these models can be both linked to structures exhibited by covariance and precision …
While these models can be both linked to structures exhibited by covariance and precision …
Estimating social opinion dynamics models from voting records
This paper aims at modeling and inferring the influence among individuals from voting data
(or more generally from actions that are selected by choosing one of m different options) …
(or more generally from actions that are selected by choosing one of m different options) …
Grid topology identification with hidden nodes via structured norm minimization
This letter studies a topology identification problem for an electric distribution grid using sign
patterns of the inverse covariance matrix of bus voltage magnitudes and angles, while …
patterns of the inverse covariance matrix of bus voltage magnitudes and angles, while …
Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion
Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of
people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose …
people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose …
Fairness-Aware Estimation of Graphical Models
This paper examines the issue of fairness in the estimation of graphical models (GMs),
particularly Gaussian, Covariance, and Ising models. These models play a vital role in …
particularly Gaussian, Covariance, and Ising models. These models play a vital role in …
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models
Exploring and detecting community structures hold significant importance in genetics, social
sciences, neuroscience, and finance. Especially in graphical models, community detection …
sciences, neuroscience, and finance. Especially in graphical models, community detection …