ICA and IVA for Data Fusion: An Overview and a New Approach Based on Disjoint Subspaces
ICA and IVA for Data Fusion: An Overview and a New Approach Based on Disjoint Subspaces
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DOI：
10.1109/lsens.2018.2884775
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发表时间：
2019-01-01
影响因子：
2.8
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Adali, Tulay;Akhonda, M. A. B. S.;Calhoun, Vince D.
数据驱动的方法对于多集和多模态数据的融合非常有吸引力，特别是使用基于独立成分分析（ICA）的矩阵分解及其对多数据集的扩展：独立向量分析（IVA）。这主要是由于独立性使得（本质上）在非常一般的条件下对大量信号进行唯一的分解，而独立的分量使它们更容易解释。在本文中，我们首先提出了一个框架，该框架为前面介绍的基于ICA和IVA的融合方法提供了一个公共保护伞，并允许我们清楚地演示这些方法设计中涉及的权衡。这就激发了不相交子空间（DS）融合新方法的引入。我们通过模拟和应用于真实数据，展示了使用ICA的DS的理想性能，用于融合多模态医学成像数据-功能磁共振成像和从一组健康对照者和执行听觉怪异任务的精神分裂症患者收集的脑电图数据。
Data-driven methods have been very attractive for fusion of both multiset and multimodal data, in particular using matrix factorizations based on independent component analysis (ICA) and its extension to multiple datasets: independent vector analysis (IVA). This is primarily due to the fact that independence enables (essentially) unique decompositions under very general conditions for a large class of signals, and independent components lend themselves to easier interpretation. In this article, we first present a framework that provides a common umbrella to previously introduced fusion methods based on ICA and IVA and allows us to clearly demonstrate the tradeoffs involved in the design of these approaches. This then motivates the introduction of a new approach for fusion of disjoint subspaces (DS). We demonstrate the desired performance of DS using ICA through simulations, as well as application to real data, for fusion of multimodal medical imaging data-functional magnetic resonance imaging and electroencephalography data collected from a group of healthy controls and patients with schizophrenia performing an auditory oddball task.