Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties.
Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties.
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DOI：
10.1109/jproc.2015.2461624
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发表时间：
2015-09-01
期刊：
影响因子：
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通讯作者：
Calhoun VD
中科院分区：
文献类型：
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作者：
Adali T;Levin-Schwartz Y;Calhoun VD
融合多组数据中的信息，以提取一组对给定任务最有用和相关的特征，这是我们今天处理的许多问题所固有的。由于通常对数据集之间的实际相互作用知之甚少，因此非常希望最大限度地减少潜在的假设。这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（伊卡），因为它提供了一个简单的生成模型，并仅使用统计独立性的假设有用的分解。独立向量分析（IVA）是伊卡的最新扩展，它通过利用数据集之间的统计相关性将伊卡推广到多个数据集，因此，正如我们在本文中讨论的那样，它为多个数据集的数据融合提供了一个有吸引力的解决方案，沿着伊卡。在本文中，我们专注于多模态数据融合的两个多变量解决方案，让多个模态充分交互，以估计共同报告所有模态的潜在特征。一种解决方案是在医学成像中得到广泛应用的联合伊卡模型，第二种是这里介绍的转置IVA模型，作为基于多集典型相关分析的方法的推广。在讨论中，我们强调多样性的作用，这两个模型实现的分解，目前他们的属性和实施细节，使用户作出明智的决定，选择一个模型沿着其相关参数。支持的模拟结果，以帮助突出这些方法的实施中的主要问题的讨论。
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.