Collaborative Research: CDS&E: Theoretical Foundations and Algorithms for L1-Norm-Based Reliable Multi-Modal Data Analysis

合作研究：CDS

• 批准号: 1808591
• 负责人: Evangelos Papalexakis
• 金额: $ 17.53万
• 依托单位: University of California-Riverside
• 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=1808591&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Theoretical; Foundations

In modern applications of science and engineering, large volumes of data are collected from diverse sensor modalities, commonly stored in the form of high-order arrays (tensors), and jointly analyzed in order to extract information about underlying phenomena. This joint tensor analysis can exploit inherent dependencies across data modalities and allow for markedly enhanced inference. Standard methods for tensor analysis rely on formulations that are sensitive to heavily corrupted points among the processed data (outliers). To counteract the destructive impact of outliers in modern data analysis (and thereto relying applications), this project will investigate new theory and robust algorithmic methods. The performance benefits of the developed tools will be evaluated in applications from the fields of data analytics, machine learning and computer vision. Thus, this research aspires to increase significantly the reliability of data-enabled research across science and engineering. Combining theoretical explorations, with practical algorithmic solutions for data analysis and experimental evaluations, this project has the potential to build significant future capacity not only for U.S. academic institutions but also for the U.S. government and industry. Thus, apart from promoting the progress of science, this project could contribute to advances in the national prosperity and welfare. In addition, research activities under this project will be integrated with education. Participating students, at both graduate and undergraduate levels, will gain important experience in optimization theory, machine learning, computer vision, and data mining, among other areas. Moreover, the project plan includes multiple STEM outreach activities and supports diversity in STEM by involving?students from underrepresented groups.In this project, the theoretical underpinnings of L1-norm tensor analysis will be investigated, with a focus on its computational hardness and exact solution. Then, based on these new foundations, efficient/practical algorithms for L1-norm tensor analysis will be explored, together with scalable and distributed software implementations. These theoretical and algorithmic investigations are expected to advance significantly the knowledge in the currently under-explored area of L1-norm tensor analysis and deliver highly impactful methodologies for outlier-resistant multimodal data processing. Next, the PIs will employ the newly developed algorithmic tools in key problems from the fields of data analytics, machine learning and computer vision. In addition, research activities under this project will be integrated with education. Participating students, at both graduate and undergraduate levels, will gain important experience in optimization theory, machine learning, computer vision, and data mining, among other areas. Moreover, the project plan includes multiple STEM outreach activities?and supports diversity in STEM by involving students from underrepresented groups.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.

在现代科学和工程应用中，从不同的传感器模态收集大量数据，通常以高阶阵列（张量）的形式存储，并进行联合分析，以提取有关潜在现象的信息。这种联合张量分析可以利用跨数据模态的固有依赖性，并允许显著增强的推理。张量分析的标准方法依赖于对处理后的数据（离群值）中严重损坏的点敏感的公式。为了抵消现代数据分析中异常值的破坏性影响（及其依赖的应用程序），该项目将研究新的理论和强大的算法方法。将在数据分析、机器学习和计算机视觉领域的应用中评估所开发工具的性能优势。因此，这项研究旨在显著提高科学和工程领域数据支持研究的可靠性。该项目将理论探索与用于数据分析和实验评估的实用算法解决方案相结合，不仅有可能为美国学术机构，而且也有可能为美国政府和行业建立重要的未来能力。因此，除了促进科学进步外，该项目还可以促进国家繁荣和福利。此外，该项目下的研究活动将与教育相结合。参与的学生，无论是研究生还是本科生，都将获得优化理论，机器学习，计算机视觉和数据挖掘等领域的重要经验。此外，该项目计划包括多个STEM外展活动，并通过参与？在这个项目中，L1范数张量分析的理论基础将被研究，重点是它的计算难度和精确解。然后，基于这些新的基础，L1范数张量分析的有效/实用算法将被探索，以及可扩展和分布式软件实现。这些理论和算法研究预计将大大推进L1范数张量分析这一目前尚未探索的领域的知识，并为抗异常值的多模态数据处理提供高度有效的方法。接下来，PI将在数据分析、机器学习和计算机视觉领域的关键问题中使用新开发的算法工具。此外，该项目下的研究活动将与教育相结合。参与的学生，无论是研究生还是本科生，都将获得优化理论，机器学习，计算机视觉和数据挖掘等领域的重要经验。此外，项目计划包括多个STEM外展活动？该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Core Consistency of a Compressed Tensor

压缩张量的核心一致性

• DOI: 10.1109/dsw.2019.8755593
• 发表时间: 2019
• 期刊: 2019 IEEE Data Science Workshop (DSW
• 作者: Tsitsikas, Georgios;Papalexakis, Evangelos E.
• 通讯作者: Papalexakis, Evangelos E.

NSVD: Normalized Singular Value Deviation Reveals Number of Latent Factors in Tensor Decomposition

NSVD：归一化奇异值偏差揭示张量分解中潜在因子的数量

• DOI: 10.1137/1.9781611976236.75
• 发表时间: 2020
• 期刊: Proceedings of the 2020 SIAM International Conference on Data Mining
• 作者: Tsitsikas, Yorgos;Papalexakis, Evangelos E.
• 通讯作者: Papalexakis, Evangelos E.

Tensorized Feature Spaces for Feature Explosion

用于特征爆炸的张化特征空间

• DOI: 10.1109/icpr48806.2021.9412320
• 发表时间: 2021
• 期刊: International Conference on Pattern Recognition (ICPR
• 作者: Pasricha, Ravdeep S.;Devineni, Pravallika;Papalexakis, Evangelos E.;Kannan, Ramakrishnan
• 通讯作者: Kannan, Ramakrishnan

Tensor-based Complementary Product Recommendation

基于张量的互补产品推荐

• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Big Data (IEEE BigData 2021
• 作者: Entezari, Negin;Papalexakis, Evangelos E.;Wang, Haixun;Rao, Sharath;Prasad, Shishir Kumar
• 通讯作者: Prasad, Shishir Kumar

OCTEN: Online Compression-Based Tensor Decomposition

OCTEN：在线基于压缩的张量分解

• DOI: 10.1109/camsap45676.2019.9022641
• 发表时间: 2019
• 期刊: 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP
• 作者: Gujral, Ekta;Pasricha, Ravdeep;Yang, Tianxiong;Papalexakis, Evangelos E.
• 通讯作者: Papalexakis, Evangelos E.