The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction
This work addresses the problem of efficient unsupervised modeling for spatio-temporal data, such as in video analysis, though it appears incremental as it builds on existing light cone concepts.
The paper tackles the challenge of modeling high-dimensional spatio-temporal data by introducing three nonparametric algorithms based on light cone decompositions, which enable tractable inference and good predictive performance for applications like full-frame video.
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional "light cones". We review light cone decompositions for predictive state reconstruction, introducing three simple light cone algorithms. These methods allow for tractable inference of spatio-temporal data, such as full-frame video. The algorithms make few assumptions on the underlying process yet have good predictive performance and can provide distributions over spatio-temporal data, enabling sophisticated probabilistic inference.