Duan Kuaikuai, Calhoun Vince D, Liu Jingyu, Silva Rogers F
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1770-1774. doi: 10.1109/EMBC44109.2020.9175277.
Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require the same number of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions.Clinical Relevance-This establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.
多模态数据融合是一个备受关注的话题。已经提出了几种融合方法来研究跨模态的连贯模式和相应的联系，例如联合独立成分分析（jICA）、多集典型相关分析（mCCA）、mCCA+jICA、使用独立成分分析的不相交子空间（DS-ICA）和平行独立成分分析。jICA利用源独立性，但假设加载参数共享。mCCA直接最大化跨模态的相关联系，但限于正交特征。虽然jICA、mCCA或两步法mCCA+jICA一起分析的模态数量没有理论限制，但这些方法只能提取共同特征，并且所有模态需要相同数量的源/成分。另一方面，DS-ICA和平行独立成分分析可以识别共同特征和不同特征，但限于两种模态。DS-ICA假设共同特征之间的加载参数共享，当联系很强时效果很好。平行独立成分分析同时最大化模态之间的相关性和源的独立性，同时允许每个模态有不同数量的源。然而，只能优化非常有限数量的模态和联系对。为了克服这些限制，我们提出了任意方式独立成分分析（aNy-way ICA），这是一种同时最大化源的独立性和跨模态相关性的新模型。aNy-way ICA通过一个无正交性约束的共享权重矩阵模型，将信息最大化独立成分分析和高斯独立向量分析（IVA-G）结合起来。仿真结果表明，aNy-way ICA不仅能准确恢复源和加载，还能恢复真实的协方差/联系模式，无论不同模态的源数量相同还是不同。此外，在源和加载恢复精度方面，特别是在有噪声的条件下，aNy-way ICA优于mCCA和mCCA+jICA。临床相关性——这建立了一个用于任意数量模态和联系对的N路数据融合模型，允许不同模态有不同数量的非正交源。
