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首页> 外文期刊>Proceedings of the IEEE >Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data
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Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data

机译：从矩阵到高阶张量的链接成分分析：对生物医学数据的应用

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摘要

With the increasing availability of various sensor technologies, we now have access to large amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be jointly analyzed to explore their latent connections. Various component analysis methods have played an increasingly important role for the analysis of such coupled data. In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications. Then, we discuss their important extensions and generalization to multiblock multiway (tensor) data. We show how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data. Special emphasis is given to the flexible common and individual feature analysis of multiblock data with the aim to simultaneously extract common and individual latent components with desired properties and types of diversity. Illustrative examples are given to demonstrate their effectiveness for biomedical data analysis.
机译：随着各种传感器技术可用性的提高，我们现在可以访问大量的多块（也称为多集，多关系或多视图）数据，需要对这些数据进行联合分析以探索其潜在联系。各种成分分析方法在这种耦合数据的分析中起着越来越重要的作用。在本文中，我们首先简要概述现有的基于矩阵的（双向）成分分析方法，以对此类数据进行联合分析，重点是生物医学应用。然后，我们讨论它们对多块多路（张量）数据的重要扩展和概括。我们展示了受约束的多块张量分解方法如何能够通过合并数据的多路性质来提取所有块共享的相似或统计相关的共同特征。特别强调多块数据的灵活的公共和个体特征分析，旨在同时提取具有所需属性和多样性类型的公共和个体潜在成分。给出了说明性实例以证明其对生物医学数据分析的有效性。

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