Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
CITIC Research Center, University of A Coruña, 15008 A Coruña, Spain.
Sensors (Basel). 2023 Jun 5;23(11):5333. doi: 10.3390/s23115333.
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.
联合盲源分离 (JBSS) 在跨多个相关数据集的潜在结构建模方面具有广泛的应用。然而，对于高维数据，JBSS 的计算量非常大，限制了可纳入可处理分析的数据集数量。此外，如果数据的真实潜在维度没有得到充分建模，JBSS 可能效果不佳，其中严重的过度参数化可能导致较差的分离和时间性能。在本文中，我们提出了一种可扩展的 JBSS 方法，通过对数据进行建模和分离“共享”子空间。共享子空间定义为所有数据集共有的潜在源的子集，由共同形成低秩结构的源组表示。我们的方法首先使用专门设计用于估计共享源的多元高斯源先验 (IVA-G) 提供高效的独立向量分析 (IVA) 初始化。然后根据估计源是否共享来评估它们，之后分别对共享和非共享源应用进一步的 JBSS。这为降低问题的维度提供了一种有效手段，从而可以用更多的数据集进行更有效的分析。我们将我们的方法应用于静息态 fMRI 数据集，证明我们的方法可以在显著降低计算成本的情况下实现出色的估计性能。
