Collaborative Research: Multimodal Sensing and Analytics at Scale: Algorithms and Applications

协作研究：大规模多模态传感和分析：算法和应用

• 批准号: 1807660
• 负责人: Nikolaos Sidiropoulos
• 金额: $ 20万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807660&HistoricalAwards=false
• 关键词: Collaborative; Research; Multimodal; Sensing; Analytics

Finding highly correlated latent factors in multimodal signals and data: Scalable algorithms and applications in sensing, imaging, and language processingAbstract: Multimodal signals and data arise naturally in many walks of science and engineering, and our digital society presents ever-increasing opportunities to collect and extract useful information from such data. For example, brain magnetic resonance imaging and electro-encephalography are two modes of sensing brain activity that can offer different "views" of the same set of patients (entities). Co-occurrence frequencies of a given set of words in different languages is another example. Crime, poverty, welfare, income, tax, school, unemployment, and other types of social data offer different views of a given set of municipalities. Integrating multiple views to extract meaningful common information is of great interest, and finds a vast amount of timely applications -- in brain imaging, machine translation, landscape change detection in remote sensing, and social science research, to name a few. However, existing multiview analytics tools -- notably (generalized) canonical correlation analysis [(G)CCA] -- are struggling to keep pace with the size of today's datasets, and the problem is only getting worse. Furthermore, the complex structure and dynamic nature of some of the underlying phenomena are not accounted for in classical GCCA. This project will provide much needed scalable and flexible computational tools for GCCA-based multimodal sensing and analytics, thereby benefiting a large variety of scientific and engineering applications. It will produce a framework allowing for plug-and-play incorporation of application-specific prior information, and distributed implementation. Beyond linear and batch GCCA, nonlinear GCCA and streaming GCCA will be considered. These are appealing and timely for many applications, but associated computational tools are sorely missing.In terms of theory and methods, many key aspects of GCCA (such as convergence properties, distributed implementation, and streaming variants) are still poorly understood. The research will provide a set of high-performance computational tools that are backed by advanced optimization theory and rigorous convergence guarantees. The research will evolve along the following synergistic thrusts: 1) scalable and stochastic GCCA algorithms; 2) distributed, streaming and nonlinear GCCA algorithms; and 3) validation, using a series of timely and important applications in remote sensing, brain imaging, natural language processing, and sensor array processing. Devising scalable, flexible, streaming, and nonlinear GCCA algorithms is very well-motivated for modern sensing and analytics problems which involve rapidly increasing amounts of data with unknown underlying dynamics. Using GCCA for large-scale dynamic and complex data also poses very challenging and exciting modeling and optimization problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在多模式信号和数据中找到高度相关的潜在因素：可扩展的算法和应用中的传感，成像和语言处理方法：多模式信号和数据自然出现在许多科学和工程步行中，而我们的数字社会却在这些数据中始终从此类数据中收集和提取有用信息。例如，大脑磁共振成像和电脑摄影是感应大脑活动的两种模式，可以提供同一组患者（实体）的不同“视图”。用不同语言的一组单词的共发生频率是另一个示例。犯罪，贫困，福利，收入，税收，学校，失业和其他类型的社交数据提供了一套特定市政当局的不同看法。集成多种观点以提取有意义的共同信息引起了人们的极大兴趣，并且发现了大量及时的应用 - 在大脑成像，机器翻译，遥感中的景观变化检测和社会科学研究中，仅举几例。 但是，现有的多视分析工具 - 尤其是（广义）规范相关分析[（g）CCA] - 正在努力与当今数据集的大小保持同步，而且问题越来越严重。此外，在经典的GCCA中未考虑某些基本现象的复杂结构和动态性质。该项目将为基于GCCA的多模式感测和分析提供急需的可扩展和灵活的计算工具，从而使各种各样的科学和工程应用受益。它将产生一个框架，允许插入插件的特定于应用程序的先验信息和分布式实现。除线性和批处理GCCA外，还将考虑非线性GCCA和流媒体GCCA。对于许多应用程序，这些都很有吸引力，但相关的计算工具却非常缺少。在理论和方法方面，GCCA的许多关键方面（例如收敛属性，分布式实现和流媒体变体）仍然很熟悉。该研究将提供一组高性能计算工具，这些工具得到了高级优化理论和严格的合并保证的支持。该研究将沿以下协同推力发展：1）可扩展和随机GCCA算法； 2）分布式，流和非线性GCCA算法； 3）验证，使用一系列及时且重要的应用程序在遥感，大脑成像，自然语言处理和传感器阵列处理中进行验证。对于现代感测和分析问题，精心促进可扩展，灵活，流和非线性GCCA算法的动机非常有能力，这些问题涉及迅速增加的数据，并具有未知的潜在动态。使用GCCA进行大规模的动态和复杂数据也提出了非常具有挑战性，令人兴奋的建模和优化问题。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的审查标准，被认为值得通过评估来获得支持。

项目成果

Tensor Completion From Regular Sub-Nyquist Samples

• DOI: 10.1109/tsp.2019.2952044
• 发表时间: 2020-01-01
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Kanatsoulis, Charilaos I.;Fu, Xiao;Akcakaya, Mehmet
• 通讯作者: Akcakaya, Mehmet

Reliable Detection of Unknown Cell-Edge Users via Canonical Correlation Analysis

• DOI: 10.1109/twc.2020.2980511
• 发表时间: 2020-03
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: M. S. Ibrahim;N. Sidiropoulos

Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems

• DOI: 10.1109/tsp.2018.2873506
• 发表时间: 2018-05
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Cheng Qian;Xiao Fu;N. Sidiropoulos;Ye Yang

Joint Graph Embedding and Alignment with Spectral Pivot

• DOI: 10.1145/3447548.3467377
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Paris A. Karakasis;Aritra Konar;N. Sidiropoulos

Bilinear factorizations subject to monomial equality constraints via tensor decompositions

通过张量分解受单项式等式约束的双线性分解

• DOI: 10.1016/j.laa.2021.03.022
• 发表时间: 2021
• 期刊: Linear Algebra and its Applications
• 影响因子: 1.1
• 作者: Sørensen, Mikael;De Lathauwer, Lieven;Sidiropoulos, Nicholaos D.
• 通讯作者: Sidiropoulos, Nicholaos D.