EAGER-DynamicData: Judicious Censoring, Random Sketching, and Efficient Validate for Learning Patterns from Dynamically-Changing and Large-Scale Data Sets

EAGER-DynamicData：明智的审查、随机草图和高效验证，用于从动态变化的大规模数据集中学习模式

基本信息

批准号：
1500713
负责人：
Georgios Giannakis
金额：
$ 30万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-15 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1500713&HistoricalAwards=false
关键词：
EAGER DynamicData Judicious Censoring Random

项目摘要

Abstract. With pervasive sensors continuously collecting and recording massive amounts of information, there is no doubt this is an era of data deluge. Learning from these dynamic and large volumes of data is expected to bring significant science and engineering advances along with consequent improvements in quality of life. The present early-concept grant for exploratory research aims to develop potentially transformative pattern recognition techniques that will be specifically tested on dynamically deforming (due to e.g., patient motion) cardiac magnetic resonance images, as well as on information extraction from large-scale healthcare datasets. Big challenges that this project addresses, include the sheer volume of online and growing datasets, which makes it impossible to run analytics especially in batch form; and also the facts that large-scale datasets are inevitably noisy, dynamic, incomplete, prone to outliers and (un)intentional misses, as well as vulnerable to cyber-attacks. The project's large-scale analytics will also permeate interdisciplinary benefits to environmental data mining, neuroscience, and the future power grid. At a broader scale, the developed technologies will provide valuable tools for foundational science and engineering research, and promote societal embracing of the emergent big data technologies, along with training the next-generation of data science professionals.This early-concept grant for exploratory research aspires to tackle big data challenges by putting forth large-scale learning tools and their performance analyses that leverage two untested, but potentially transformative, ideas for extracting computationally affordable yet informative subsets of massive and dynamic datasets, namely i) adaptive censoring, and ii) random data sketching-and-validation. Data in this project can be stationary or nonstationary; they become available in batch or sequential (a.k.a. online) modes; they can be collected in vectors, matrices or general multi-way arrays (called tensors); noise, possibly outliers and (un)intentional misses are present; and data processing can be linear or nonlinear in adaptive or non-adaptive modes. The proposed high risk-high payoff research lies at the intersection of essential big data tools including compressive sampling, matrix and tensor completion, anomaly and outlier identification, online and parallel optimization techniques. In accordance with the major inference tasks, three intertwined research thrusts will be pursued: T1) Adaptive censoring for large-scale regressions; T2) Subspace tracking and imputation for dynamic large-scale tensors; and T3) Sketch-and-validate for large-scale clustering and classification. The resultant innovative tools will be tested in healthcare data, and multi-dimensional magnetic resonance imaging, having as ultimate goal high-resolution biomedical movies to be acquired, processed, and displayed in real time.

抽象的。随着普遍传感器不断收集和记录大量信息，毫无疑问，这是数据洪水的时代。从这些动态和大量数据中学习将带来重大的科学和工程进步，并改善生活质量。目前对探索性研究的早期概念赠款旨在开发潜在的变革性模式识别技术，这些技术将在动态变形（例如患者运动）心脏磁共振图像以及从大型医疗保健数据集中进行的信息提取。该项目解决的重大挑战包括在线和不断增长的数据集，这使得无法运行分析，尤其是以批量形式进行。而且，大规模数据集不可避免地嘈杂，动态，不完整，容易出现异常值和（联合国）故意错过，并且容易受到网络攻击的事实。该项目的大规模分析还将渗透到环境数据挖掘，神经科学和未来电网的跨学科利益。 At a broader scale, the developed technologies will provide valuable tools for foundational science and engineering research, and promote societal embracing of the emergent big data technologies, along with training the next-generation of data science professionals.This early-concept grant for exploratory research aspires to tackle big data challenges by putting forth large-scale learning tools and their performance analyses that leverage two untested, but potentially transformative, ideas for extracting computationally affordable然而，大规模和动态数据集的内容丰富的子集，即i）自适应审查，ii）随机数据草图和验证。该项目中的数据可能是固定的或非平稳的；它们以批处理或顺序（又称在线）模式提供；它们可以收集到向量，矩阵或一般多路阵列（称为张量）中；噪音，可能是离群值和（联合国）故意错过的；数据处理可以是线性或非自适应模式中的线性或非线性。拟议的高风险高收益研究在于必需的大数据工具的交集，包括压缩抽样，矩阵和张量完成，异常和异常标识，在线和并行优化技术。根据主要的推理任务，将提出三个相互交织的研究推力：T1）对大规模回归的自适应审查； T2）动态大规模张量的子空间跟踪和插定；和T3）草图和估算的大规模聚类和分类。最终的创新工具将在医疗保健数据和多维磁共振成像中进行测试，并将其作为最终目标高分辨率生物医学电影的实时测试。