In recent years, Prof Ortega and his team have focused their research on the development of novel tools for Graph Signal Processing (GSP). GSP methods can be used to analyze sensor and communication networks, traffic networks and electrical grids, online social networks, as well as graphs associated to machine learning tasks. On the theoretical front, this work has focused on designing graph filters, anomaly detection, graph sampling and learning graphs from data. These methods are being applied to the various applications currently studied in IMSC. As an example, within the Health domain, his group is developing approaches for human activity analysis using GSP on the graph connecting the estimated positions of body joints (i.e., the skeleton graph). This work has been applied for motion analysis in Parkinson’s Disease patients and also being explored for manufacturing applications. As another example, in the Transportation domain, GSP can be used to analyze traffic patterns and detect dependencies in traffic flows across cities. Sensor networks deployed in Smart Cities applications can benefit from sampling methods developed in GSP, which can also be used to detect anomalies, indicative of sensor malfunction. Lastly, both sampling and filtering can be applied to analyze information collected from online social networks.
Kao, J.-Y., Nguyen, M., Nocera, L., Shahabi, C., Ortega, A., Winstein, C., Sorkhoh, I., Chung, Y.-c., Chen, Y.-a., and Bacon, H. Validation of automated mobility assessment using a single 3d sensor. In European Conference on Computer Vision (2016), Springer, pp. 162–177.
Kao, J.-Y., Ortega, A., and Narayanan, S. S. Graph-based approach for motion capture data representation and analysis. In Image Processing (ICIP), 2014 IEEE International Conference on (2014), IEEE, pp. 2061–2065.
Egilmez, H. E., Pavez, E., and Ortega, A. Graph learning from data under Laplacian and structural constraints. IEEE Journal of Selected Topics in Signal Processing 11, 6 (2017), 825–841.
Gadde, A., Anis, A., and Ortega, A. Active semi-supervised learning using sampling theory for graph signals. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (2014), ACM, pp. 492–501.
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine 30, 3 (2013), 83–98.