Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

• URL: http://arxiv.org/abs/2002.05578v5
• Date: Fri, 14 Aug 2020 23:34:16 GMT
• Title: Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
• Authors: Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, and Rose Yu
• Abstract summary: We develop a novel Multiresolution Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. When applied to two real-world datasets, MRTL demonstrates 45x speedup compared to a fixed resolution approach.
• Score: 44.89716235936401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 4~5x speedup compared to a fixed resolution approach while yielding accurate and interpretable latent factors.

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