2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-10.2
Paper Title Graph-Adaptive Incremental learning using an ensemble of Gaussian process experts
Authors Konstantinos D. Polyzos, Qin Lu, Georgios B. Giannakis, University of Minnesota, United States
SessionSPTM-10: Distributed Learning over Graphs
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of network science applications. However, most SSL approaches rely on deterministic similarity metrics for prediction, thus providing only point estimates of the sought function. To allow for uncertainty quantification, which is of utmost importance in safety-critical applications, this work tackles the SSL task in a Gaussian process (GP) based Bayesian framework to propagate the distribution of nonparametric function estimates. Specifically, an incremental learning scenario is considered, where prediction of the desired value of a new node per iteration is followed by processing the corresponding nodal observation. Capitalizing on random features for scalability, an ensemble of GP experts is employed, each associated with a unique kernel from a known dictionary, to choose the fitted kernel combination in a data adaptive fashion, thus bypassing the need for offline model training. Experiments with synthetic and real data showcase the merits of the proposed approach.