Generalized Liquid Association Analysis for Multimodal Data Integration
Generalized Liquid Association Analysis for Multimodal Data Integration
复制标题
DOI：
10.1080/01621459.2021.2024437
复制
发表时间：
2020-08
影响因子：
3.7
通讯作者：
Lexin Li;Jing Zeng;Xin Zhang
中科院分区：
文献类型：
--
作者：
Lexin Li;Jing Zeng;Xin Zhang
摘要多模态数据是目前科学研究的主流。多模态整合分析的核心问题之一是了解在给定另一模态或人口统计变量的情况下，两种数据模态如何相互关联和相互作用。这个问题可以表述为研究三组随机变量之间的关联，这个问题在文献中受到的关注相对较少。在本文中，我们提出了一种新的广义液体关联分析方法，为研究这类重要的三元关联问题提供了一个新的独特的角度。我们将液体关联的概念从单变量设置扩展到稀疏，多变量和高维设置。我们建立了种群降维模型，将问题转化为三向张量的稀疏Tucker分解，并开发了一种高阶正交迭代算法进行参数估计。我们导出了该估计量的非渐近误差界和渐近一致性，同时允许变量维大于样本大小并随样本大小发散。我们通过模拟和多模态神经成像应用于阿尔茨海默病研究证明了该方法的有效性。本文的补充材料可在网上获得。
Abstract Multimodal data are now prevailing in scientific research. One of the central questions in multimodal integrative analysis is to understand how two data modalities associate and interact with each other given another modality or demographic variables. The problem can be formulated as studying the associations among three sets of random variables, a question that has received relatively less attention in the literature. In this article, we propose a novel generalized liquid association analysis method, which offers a new and unique angle to this important class of problems of studying three-way associations. We extend the notion of liquid association from the univariate setting to the sparse, multivariate, and high-dimensional setting. We establish a population dimension reduction model, transform the problem to sparse Tucker decomposition of a three-way tensor, and develop a higher-order orthogonal iteration algorithm for parameter estimation. We derive the nonasymptotic error bound and asymptotic consistency of the proposed estimator, while allowing the variable dimensions to be larger than and diverge with the sample size. We demonstrate the efficacy of the method through both simulations and a multimodal neuroimaging application for Alzheimer’s disease research. Supplementary materials for this article are available online.