Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park,u00a0Kenneth Carr,u00a0Stephan Zheng,u00a0Yisong Yue,u00a0Rose Yu
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent factor models can describe higher-order correlations for spatial data. However, they are computationally expensive to train and are sensitive to initialization, leading to spatially incoherent, uninterpretable results. We develop a novel Multiresolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution to reduce computation. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4u00a05x speedup compared to a fixed resolution approach while yielding accurate and interpretable latent factors.
