1 research outputs found
Adaptive Sensing for Learning Nonstationary Environment Models
Most environmental phenomena, such as wind profiles, ozone concentration and
sunlight distribution under a forest canopy, exhibit nonstationary dynamics
i.e. phenomenon variation change depending on the location and time of
occurrence. Non-stationary dynamics pose both theoretical and practical
challenges to statistical machine learning algorithms aiming to accurately
capture the complexities governing the evolution of such processes. In this
paper, we address the sampling aspects of the problem of learning nonstationary
spatio-temporal models, and propose an efficient yet simple algorithm - LISAL.
The core idea in LISAL is to learn two models using Gaussian processes (GPs)
wherein the first is a nonstationary GP directly modeling the phenomenon. The
second model uses a stationary GP representing a latent space corresponding to
changes in dynamics, or the nonstationarity characteristics of the first model.
LISAL involves adaptively sampling the latent space dynamics using information
theory quantities to reduce the computational cost during the learning phase.
The relevance of LISAL is extensively validated using multiple real world
datasets.Comment: ArXiv version of the paper written in 201