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Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
2014
Neural Information Processing Systems
Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among
dblp:conf/nips/BahadoriY014
fatcat:j5uldge4tvaeze3f2e3jkymnqq