Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
复制标题
DOI：
--
复制
发表时间：
2020-02
期刊：
影响因子：
--
通讯作者：
Jung Yeon Park;K. T. Carr;Stephan Zhang;Yisong Yue;Rose Yu
中科院分区：
文献类型：
--
作者：
Jung Yeon Park;K. T. Carr;Stephan Zhang;Yisong Yue;Rose Yu
高效且可解释的空间分析在地质学、体育和气候科学等许多领域都至关重要。张量潜在因子模型能够描述空间数据的高阶相关性。然而，它们训练起来计算成本高昂，且对初始化敏感，导致空间上不连贯、无法解释的结果。我们开发了一种新颖的多分辨率张量学习（MRTL）算法，用于高效学习可解释的空间模式。MRTL从一个近似满秩张量模型初始化潜在因子以提高可解释性，并从粗分辨率逐步学习到细分辨率以减少计算量。我们还证明了MRTL的理论收敛性和计算复杂性。当应用于两个真实世界的数据集时，与固定分辨率方法相比，MRTL展示出4 - 5倍的加速，同时产生准确且可解释的潜在因子。
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.