350 research outputs found
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
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 outperforms state-of-the-art approaches
Matrix completion and extrapolation via kernel regression
Matrix completion and extrapolation (MCEX) are dealt with here over
reproducing kernel Hilbert spaces (RKHSs) in order to account for prior
information present in the available data. Aiming at a faster and
low-complexity solver, the task is formulated as a kernel ridge regression. The
resultant MCEX algorithm can also afford online implementation, while the class
of kernel functions also encompasses several existing approaches to MC with
prior information. Numerical tests on synthetic and real datasets show that the
novel approach performs faster than widespread methods such as alternating
least squares (ALS) or stochastic gradient descent (SGD), and that the recovery
error is reduced, especially when dealing with noisy data
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