BIGDATA: F: Algorithms for Tensor-Based Modeling of Large Scale Structured Data

BIGDATA：F：大规模结构化数据基于张量的建模算法

• 批准号: 1837985
• 负责人: Kayvan Najarian
• 金额: $ 141.89万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837985&HistoricalAwards=false
• 关键词: BIGDATA; Algorithms; Tensor; Based; Modeling

The project aims to develop efficient algorithms for big data applications by exploiting its rich structure. Big data often contains higher-dimensional arrays. One dimensional arrays are vectors and two dimensional arrays are matrices. Arrays of dimension three or more are called tensors and have a complex structure. Some of the challenges that the research team will address are noise removal, recovery of missing data by inference and data size reduction by distilling relevant information. The resulting methods will be applied to early detection of sepsis, which contributes to the death of 200,000 people in the United States every year.Most algorithms for low rank structured tensors are based on the Canonical Polyadic decomposition (also known as CP, Parafac or Candecomp). These algorithms may converge slowly, are numerically unstable and are difficult to scale to big data. The theoretical framework for tensor decompositions that is used is based on an algebraic graphical calculus that utilizes colored Brauer diagrams to obtain explicit formulas. The graphical calculus will also be used to give accurate estimates for the computational and memory complexity of these algorithms. The investigators will design efficient, numerically stable and computationally feasible algorithms for crucial tensor operations that are widely applicable to big data applications. Specifically, the project isexpected to create fast, scalable and reliable algorithms for tensor analysis, and apply them to crucial big data tasks including noise removal, dimension reduction, imputation of missing data and classification in a variety of applications with structured data.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.

该项目旨在通过利用其丰富的结构为大数据应用开发高效的算法。大数据通常包含更高维的数组。一维数组是向量，二维数组是矩阵。三维或三维以上的数组称为张量，具有复杂的结构。研究团队将解决的一些挑战是消除噪声，通过推理恢复丢失的数据，以及通过提取相关信息来减少数据大小。由此产生的方法将被应用于败血症的早期检测，败血症每年导致美国20万人死亡。大多数低秩结构张量的算法都是基于典型多进分解（也称为CP，Parafac或Candecomp）。这些算法可能收敛缓慢，数值不稳定，并且难以扩展到大数据。所使用的张量分解的理论框架是基于代数图形演算，利用彩色布劳尔图获得明确的公式。图形演算也将被用来准确估计这些算法的计算和内存的复杂性。研究人员将为广泛适用于大数据应用的关键张量运算设计高效、数值稳定和计算可行的算法。具体而言，该项目有望为张量分析创建快速，可扩展和可靠的算法，并将其应用于关键的大数据任务，包括去噪，降维，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

项目成果

Algebraic Methods for Tensor Data

张量数据的代数方法

• DOI: 10.1137/19m1272494
• 发表时间: 2021
• 期刊: SIAM Journal on Applied Algebra and Geometry
• 影响因子: 1.2
• 作者: Tokcan, Neriman;Gryak, Jonathan;Najarian, Kayvan;Derksen, Harm
• 通讯作者: Derksen, Harm

Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification

• DOI: 10.3390/a16020104
• 发表时间: 2023-02-01
• 期刊: ALGORITHMS
• 影响因子: 2.3
• 作者: Minoccheri，Cristian;Alge，Olivia;Derksen，Harm
• 通讯作者: Derksen，Harm