Adali Tülay, Levin-Schwartz Yuri, Calhoun Vince D
Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
University of New Mexico and the Mind Research Network, Albuquerque, NM 87106, USA.
Proc IEEE Inst Electr Electron Eng. 2015 Sep 1;103(9):1478-93. doi: 10.1109/JPROC.2015.2461624.
Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA) generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple datasets along with ICA. In this paper, we focus on two multivariate solutions for multi-modal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set canonical correlation analysis. In the discussion, we emphasize the role of diversity in the decompositions achieved by these two models, present their properties and implementation details to enable the user make informed decisions on the selection of a model along with its associated parameters. Discussions are supported by simulation results to help highlight the main issues in the implementation of these methods.
为了提取对给定任务最有用且相关的一组特征，融合来自多组数据的信息是我们如今处理的许多问题所固有的。由于通常对数据集之间的实际交互了解甚少，所以非常希望尽量减少潜在假设。这就是数据驱动方法日益重要的主要原因，特别是独立成分分析（ICA），因为它通过一个简单的生成模型且仅利用统计独立性假设提供了有用的分解。ICA的一个最新扩展，独立向量分析（IVA），通过利用跨数据集的统计依赖性将ICA推广到多个数据集，因此，正如我们在本文中所讨论的，它与ICA一起为来自多个数据集的数据融合提供了一个有吸引力的解决方案。在本文中，我们专注于多模态数据融合的两种多变量解决方案，这两种方案能让多个模态充分交互以估计共同反映所有模态的潜在特征。一种解决方案是在医学成像中已得到广泛应用的联合ICA模型，另一种是本文引入的转置IVA模型，它是基于多集典型相关分析的一种方法的推广。在讨论中，我们强调了多样性在这两种模型所实现的分解中的作用，介绍了它们的特性和实现细节，以便用户能就模型及其相关参数的选择做出明智决策。讨论有仿真结果支持，以帮助突出这些方法实现中的主要问题。
