Double-Matched Matrix Decomposition for Multi-View Data
Double-Matched Matrix Decomposition for Multi-View Data
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DOI：
10.1080/10618600.2022.2067860
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发表时间：
2021-05
影响因子：
2.4
通讯作者：
Dongbang Yuan;Irina Gaynanova
中科院分区：
文献类型：
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作者：
Dongbang Yuan;Irina Gaynanova
摘要研究了从多视点数据中提取联合信号和单个信号的问题，多视点数据是指从不同来源采集的匹配样本数据。虽然现有的方法多视图数据分解探索单匹配的数据样本，我们专注于双匹配的多视图数据（样本和源功能匹配）。我们的激励性例子是从同一受试者的原发性肿瘤和正常组织中收集的miRNA数据;因此，来自两种组织的测量结果在受试者和miRNA之间都是匹配的。我们提出的双匹配矩阵分解允许我们同时提取跨受试者的联合和个体信号，以及跨miRNA的联合和个体信号。我们的估计方法利用双匹配制定一种新型的优化问题，明确的行空间和列空间的限制，我们开发了一个有效的迭代算法。数值研究表明，与现有的基于单匹配的多视图数据分解相比，利用双匹配可以获得更优越的上级信号估计性能。我们将我们的方法应用于miRNA数据以及英超联赛足球比赛的数据，并找到与特定领域知识一致的联合和个人多视图信号。本文的补充材料可在网上查阅。
Abstract We consider the problem of extracting joint and individual signals from multi-view data, that is, data collected from different sources on matched samples. While existing methods for multi-view data decomposition explore single matching of data by samples, we focus on double-matched multi-view data (matched by both samples and source features). Our motivating example is the miRNA data collected from both primary tumor and normal tissues of the same subjects; the measurements from two tissues are thus matched both by subjects and by miRNAs. Our proposed double-matched matrix decomposition allows us to simultaneously extract joint and individual signals across subjects, as well as joint and individual signals across miRNAs. Our estimation approach takes advantage of double-matching by formulating a new type of optimization problem with explicit row space and column space constraints, for which we develop an efficient iterative algorithm. Numerical studies indicate that taking advantage of double-matching leads to superior signal estimation performance compared to existing multi-view data decomposition based on single-matching. We apply our method to miRNA data as well as data from the English Premier League soccer matches and find joint and individual multi-view signals that align with domain-specific knowledge. Supplementary materials for this article are available online.