We consider the problem of learning a graph from a given set of smooth graph signals. Our
graph learning approach is formulated as a constrained quadratic program in the edge …

Graph-based methods are promising approaches for traditional and modern techniques in
change detection (CD) applications. Nonetheless, some graph-based approaches omit the …

We consider network topology identification subject to a signal smoothness prior on the
nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently …

Data driven graph constructions are often used in machine learning applications. However,
learning an optimal graph from data is still a challenging task. K-nearest neighbor and …

Human labeling is time-consuming and costly. This problem is further exacerbated in
extremely imbalanced class label scenarios, such as detecting fraudsters in online websites …

Graph learning plays an important role in many data mining and machine learning tasks,
such as manifold learning, data representation and analysis, dimensionality reduction, data …

Hypergraphs are vital in modelling data with higher-order relations containing more than two
entities, gaining prominence in machine learning and signal processing. Many hypergraph …

Graph learning methods have recently been receiving increasing interest as means to infer
structure in datasets. Most of the recent approaches focus on different relationships between …

We develop online graph learning algorithms from streaming network data. Our goal is to
track the (possibly) time-varying network topology, and affect memory and computational …

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 …