IEEE 12th International Conference on Data Mining,
IEEE, Brussels, Belgium (2012), pp. 151-160
Yang Chen, Feng Chen, Jing Dai, T.
Charles Clancy, Yao-Jan Wu
This paper describes an efficient and effective design of Robust Spatio-Temporal
Prediction based on Student’s t distribution, namely, St-RSTP, to provide
estimations based on observations over spatio-temporal neighbors. The proposed
St-RSTP is more resilient to outliers or other small departures from model
assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model.
STRE is a state-of-the-art statistical model with linear order complexity for large
scale processing. However, it assumes Gaussian observations, which has the
well-known limitation of non-robustness. In our St-RSTP design, the measurement
error follows Student’s t distribution, instead of a traditional Gaussian
distribution. This design reduces the influence of outliers, improves prediction
quality, and keeps the problem analytically intractable. We propose a novel
approximate inference approach, which approximates the model into the form that
separates the high dimensional latent variables into groups, and then estimates the
posterior distributions of different groups of variables separately in the
framework of Expectation Propagation. As a good property, our approximate approach
degeneralizes to the standard STRE based prediction, when the degree of freedom of
the Student’s t distribution is set to infinite. Extensive experimental evaluations
based on both simulation and real-life data sets demonstrated the robustness and
the efficiency of our Student-t prediction model. The proposed approach provides
critical functionality for stochastic processes on spatio-temporal data.