论文标题
多分辨率张量学习，以进行有效且可解释的空间分析
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
论文作者
论文摘要
在地质，体育和气候科学等许多领域，有效且可解释的空间分析至关重要。张量潜在因子模型可以描述空间数据的高阶相关性。但是，它们在训练上的计算昂贵，并且对初始化敏感，从而导致空间不一致，无法解释的结果。我们开发了一种新型的多分辨率张量学习（MRTL）算法，以有效地学习可解释的空间模式。 MRTL从近似的全量张量模型中初始化潜在因子，以提高可解释性，并逐渐从粗分辨率到精细分辨率以减少计算。我们还证明了MRTL的理论收敛性和计算复杂性。当应用于两个现实世界数据集时，与固定分辨率方法相比，MRTL表现出4〜5倍的速度，同时产生了准确且可解释的潜在因素。
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.
