This paper tackles a classification problem involving network data, where class-specific graphs are learned from labeled signals to obtain discriminative ...

Jan 1, 2021 · This is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly.

Mar 29, 2021 · To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph ...

This paper tackles a classification problem involving network data, where class-specific graphs are learned from labeled signals to obtain discriminative ...

Introducing discriminative graph learning for graph signal classification. Tackling dynamic environments and online topology inference.

This paper addresses the problem of learning multiple graphs from heterogeneous data by formulating an optimization problem for joint graph signal ...

Co-authors ; Online discriminative graph learning from multi-class smooth signals. SS Saboksayr, G Mateos, M Cetin. Signal Processing 186, 108101, 2021. 36, 2021.

Saboksayr, G. Mateos, and M. Cetin, “Online discriminative graph learning from multi-class smooth signals,” Signal Processing, vol. 186,.

Cetin, “Online discriminative graph learning from multi-class smooth signals,”Signal Processing, vol. 186,p. 108101, 2021. ▷ Session: Signal Processing over ...

Mar 26, 2021 · Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks.