EAGER: Nonlinear and Data-Adaptive Compressive Sampling for Big Data Processing

EAGER：用于大数据处理的非线性和数据自适应压缩采样

• 批准号: 1632865
• 负责人: Konstantinos Slavakis
• 金额: $ 11.42万
• 依托单位: SUNY at Buffalo
• 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: 2015-12-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632865&HistoricalAwards=false
• 关键词: EAGER; Nonlinear; Data; Adaptive; Compressive

As pervasive sensors continuously collect and record massive amounts ofhigh-dimensional data from communication, social, and biological networks,and growing storage as well as processing capacities of modern computershave provided new and powerful ways to dig into such huge quantities ofinformation, the need for novel analytic tools to comb through these "bigdata" becomes imperative. The objective of this project is to develop anovel framework for nonlinear, data-adaptive (de)compression algorithms tolearn the latent structure within large-scale, incomplete or corrupteddatasets for compressing and storing only the essential information, forrunning analytics in real time, inferring missing pieces of a dataset, andfor reconstructing the original data from their compressed renditions. The intellectual merit lies in the exploration of the fertile but largelyunexplored areas of manifold learning, nonlinear dimensionality reduction,and sparsity-aware techniques for compression and recovery of missing andcompromised measurements. Capitalizing on recent advances in machinelearning and signal processing, differential geometry, sparsity, anddictionary learning are envisioned as key enablers. Effort will be put alsointo developing online and distributed (non)linear dimensionality reductionalgorithms to allow for streaming analytics of sequential measurementsusing parallel processors. The broader impact is to contribute to the development of novel computational methods and tools useful for data inference, cleansing, forecasting, and collaborative filtering, with direct impact to statistical signal processing and machine learning applicationsto large-scale data analysis, including communication, social, andbiological networks.

随着无处不在的传感器不断收集和记录来自通信、社会和生物网络的海量高维数据，以及现代计算机不断增长的存储和处理能力提供了新的和强大的方法来挖掘如此大量的信息，对新型分析工具的需求变得迫切。该项目的目标是开发一种新的非线性、数据自适应(De)压缩算法框架，以了解大规模、不完整或损坏的数据集中的潜在结构，以便仅压缩和存储基本信息，进行实时分析，推断数据集的缺失片段，并从压缩后的再现中重建原始数据。智能的优点在于探索肥沃但尚未探索的领域，如流形学习、非线性降维以及稀疏性感知技术，用于压缩和恢复丢失和受损的测量。利用机器学习和信号处理的最新进展，微分几何、稀疏性和词典学习被视为关键的推动因素。还将努力开发在线和分布式(非线性)降维算法，以便能够使用并行处理器对顺序测量进行流分析。更广泛的影响是促进开发可用于数据推理、净化、预测和协作过滤的新的计算方法和工具，对统计信号处理和机器学习应用产生直接影响，用于大规模数据分析，包括通信、社会和生物网络。