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CIF: Small: Source Separation with an Adaptive Structure for Multi-Modal Data Fusion

CIF：小型：具有自适应结构的源分离，用于多模态数据融合

基本信息

批准号：
1618551
负责人：
Tulay Adali
金额：
$ 46.16万
依托单位：
University of Maryland Baltimore County
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-15 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618551&HistoricalAwards=false
关键词：
CIF Small Source Separation Adaptive

项目摘要

In many fields today, information about a given phenomenon is obtained through different types of acquisition techniques and experimental conditions, and the availability of such multimodal data has been growing. Joint analysis of this data---its fusion---promises a more comprehensive and informative view of the task at hand, and, if probed carefully, may open new venues and answer questions we might not have even thought of asking when working with a single modality. There is now significant activity in data fusion in disciplines spanning medical imaging, remote sensing, speech processing, behavioral sciences, and metabolomics, to name a few. A common challenge across multiple disciplines is determining how and to what degree different datasets are related, i.e., identifying the common and distinct subspaces across multiple datasets in terms of the information they provide for the given task.The current work provides a powerful solution to this key challenge enabling identification of the relationship among multiple datasets in a completely data-driven manner such that both the common and distinct subspaces within each dataset can be robustly identified. By combining this adaptive scheme with the well defined but flexible framework of independent vector analysis (IVA), a new powerful framework, IVA with an adaptive subspace structure (IVA-AS) is developed for effective fusion of both the multiset and multimodal data. The successful application of this framework is demonstrated using a unique medical dataset that allows the study of commonalities and differences in brain function while driving with distractions or under the influence of alcohol.

在当今的许多领域，有关给定现象的信息是通过不同类型的采集技术和实验条件获得的，并且此类多模式数据的可用性一直在增长。对这些数据的联合分析-其融合-承诺对手头的任务有一个更全面、更翔实的看法，如果仔细探索，可能会开辟新的途径，回答我们在使用单一模式工作时甚至可能没有想到要问的问题。目前，数据融合在医学成像、遥感、语音处理、行为科学和代谢组学等学科中有着重要的应用。跨多个学科的共同挑战是确定不同数据集如何以及在何种程度上相关，即，根据多个数据集为给定任务提供的信息，识别多个数据集之间的公共和不同子空间。目前的工作为这一关键挑战提供了一个强大的解决方案，能够以完全数据驱动的方式识别多个数据集之间的关系，从而可以鲁棒地识别每个数据集中的公共和不同子空间。通过结合这种自适应计划与独立向量分析（IVA），一个新的强大的框架，IVA与自适应子空间结构（IVA-AS）的定义良好，但灵活的框架开发的多集和多模态数据的有效融合。使用独特的医学数据集证明了该框架的成功应用，该数据集允许研究在分心或酒精影响下驾驶时大脑功能的共性和差异。