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

随着无处不在的传感器不断收集和记录来自通信、社会和生物网络的大量高维数据，以及现代计算机不断增长的存储和处理能力，为挖掘如此大量的信息提供了新的强大方法，对新的分析工具的需求变得势在必行。这个项目的目标是开发一个新的框架，用于非线性的，数据自适应（解）压缩算法，以学习大规模的，不完整的或损坏的数据集内的潜在结构，用于压缩和存储只有必要的信息，用于在真实的时间运行分析，推断数据集的缺失片段，并从压缩的再现中重建原始数据。其智力价值在于探索了丰富但尚未探索的领域，如流形学习、非线性降维和稀疏感知技术，用于压缩和恢复丢失和受损的测量结果。利用机器学习和信号处理的最新进展，微分几何，稀疏性和字典学习被认为是关键的推动者。努力也将投入到开发在线和分布式（非）线性降维算法，以允许使用并行处理器的连续测量的流分析。更广泛的影响是有助于开发用于数据推理、清理、预测和协同过滤的新型计算方法和工具，对统计信号处理和机器学习应用以及大规模数据分析（包括通信）产生直接影响，社会和生物网络。

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.