Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

Abstract:

Efficient and interpretable spatial analysis is crucial in many fields such as geology, 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 for boosted efficiency. We also prove the theoretical convergence and computational complexity of MRTL. When applied to a real-world climate dataset, MRTL demonstrates 4~5x speedup compared to a fixed resolution approach while yielding accurate and interpretable models.