Multidataset Independent Subspace Analysis With Application to Multimodal Fusion.
Multidataset Independent Subspace Analysis With Application to Multimodal Fusion.
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DOI：
10.1109/tip.2020.3028452
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Silva RF;Plis SM;Adali T;Pattichis MS;Calhoun VD
无监督潜变量模型--尤其是盲源分离(BSS)--因其可解释性而享有盛誉。但它们很少将多个数据集中可用的丰富多样性的信息结合在一起，即使多个数据集产生了有洞察力的联合解决方案，否则无法单独使用。我们提出了一种利用多维子空间结构的多数据集合并的直接、原则性方法。反过来，我们扩展了BSS模型，以捕获数据集之间和数据集内共享和唯一可变性的底层模式。我们的方法以灵活和协同的方式利用来自不同数据集的联合信息。我们称这种方法为多数据集独立子空间分析(MISA)。将Kotz分布用于子空间建模的方法创新，与一种新的避免局部极小值的组合优化相结合，使MISA能够在单个统一模型中产生独立分量分析(ICA)、独立向量分析(IVA)和独立子空间分析(ISA)的稳健推广。我们强调了MISA在多模式信息融合中的应用，包括样本贫乏(N=600)和低信噪比，促进了在单模式和多模式脑成像数据中的新应用。
Unsupervised latent variable models—blind source separation (BSS) especially—enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes (N = 600) and low signal-to-noise ratio, promoting novel applications in both unimodal and multimodal brain imaging data.