Paper Info
Title
Matrix Completion With Variational Graph Autoencoders: Application In Hyperlocal Air Quality Inference
Abstract
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model out-performs state-of-the-art approaches.
Year
DOI
Venue
2018
10.1109/icassp.2019.8683787
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
air quality inference, variational graph autoencoder, graph-based matrix completion, deep learning
Graph,Matrix completion,Hyperlocal,Inference,Air quality index,Data type,Artificial intelligence,Air pollution,Image resolution,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
abs/1811.01662
1520-6149
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Tien Huu Do133.08
Duc Minh Nguyen24310.96
Evaggelia Tsiligianni3123.51
Angel Lopez Aguirre400.34
Valerio Panzica La Manna5757.29
Frank J. Pasveer611.71
W Philips74816.19
Nikos Deligiannis831137.12