# Online discriminative graph learning from multi-class smooth signals

@article{Saboksayr2021OnlineDG, title={Online discriminative graph learning from multi-class smooth signals}, author={Seyed Saman Saboksayr and Gonzalo Mateos and M{\"u}jdat Çetin}, journal={Signal Process.}, year={2021}, volume={186}, pages={108101} }

## 12 Citations

### Online Graph Learning under Smoothness Priors

- Computer Science2021 29th European Signal Processing Conference (EUSIPCO)
- 2021

This work develops novel algorithms for online network topology inference given streaming observations assumed to be smooth on the sought graph, and establishes that the online graph learning algorithm converges to within a neighborhood of the optimal time-varying batch solution.

### EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks

- Computer Science2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- 2021

The novel spatio-temporal attention neural network (STANN) is proposed to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory.

### Dual-based Online Learning of Dynamic Network Topologies

- Computer Science
- 2022

This work leverages and truncates dual-based proximal gradient iterations to solve a composite smoothness-regularized, time-varying inverse problem and shows that the online DPG algorithm converges faster than a primal-based baseline of comparable complexity.

### Multi-scale and multi-layer perceptron hybrid method for bearings fault diagnosis

- International Journal of Mechanical Sciences
- 2022

### Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

- Computer Science
- 2022

An expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property is designed and a novel online EM algorithm for inference from streaming data is proposed.

### Simultaneous Graph Signal Clustering and Graph Learning

- Computer ScienceICML
- 2022

This paper addresses the problem of learning multiple graphs from heterogeneous data by formulating an optimization problem for joint graph signal clustering and graph topology inference and extends spectral clustering by partitioning the graph signals not only based on their pairwise similarities but also their smoothness with respect to the graphs associated with the clusters.

### Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection

- Computer ScienceIEEE Transactions on Geoscience and Remote Sensing
- 2022

A framework for CD based on graph fusion and driven by graph signal smoothness representation is proposed, which outperformed state-of-the-art approaches in ten out of 14 datasets.

### Learning Time-Varying Graphs From Online Data

- Computer ScienceIEEE Open Journal of Signal Processing
- 2022

This work proposes an algorithmic framework to learn time-varying graphs from online data that is model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems.

### Online Graph Learning In Dynamic Environments

- Computer Science2022 30th European Signal Processing Conference (EUSIPCO)
- 2022

Experimental results support that the proposed graph learning method is superior to the state-of-the-art methods, and dynamic regret analysis of the proposed method is performed, illustrating theoretically that proper dynamic priors do reduce regret.

### Online Graph Learning From Time-Varying Structural Equation Models

- Computer Science, Mathematics2021 55th Asilomar Conference on Signals, Systems, and Computers
- 2021

This paper focuses on a time-varying version of the structural equation modeling (SEM) framework, which is an umbrella of multivariate techniques widely adopted in econometrics, epidemiology and psychology and views the linear SEM as a first-order diffusion of a signal over a graph whose topology changes over time.

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- Computer Science2021 29th European Signal Processing Conference (EUSIPCO)
- 2021

This work develops novel algorithms for online network topology inference given streaming observations assumed to be smooth on the sought graph, and establishes that the online graph learning algorithm converges to within a neighborhood of the optimal time-varying batch solution.

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