EAGER-DynamicData: Subspace Learning From Binary Sensing

EAGER-DynamicData：从二进制感知中学习子空间

• 批准号: 1833553
• 负责人: Yuejie Chi
• 金额: $ 8.18万
• 依托单位: Carnegie-Mellon University
• 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: 2018-01-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1833553&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Subspace; Learning; Binary

Decentralized sensing systems play an increasingly critical role in everyday life, including wireless sensor networks, mobile crowd-sensing with internet-of-things, and crowdsourcing with human workers, with applications in network analysis, distributed wideband spectrum sensing, target tracking, environmental monitoring, and advertisement prediction. Despite the promise, however, efficient inference is extremely challenging due to processing large amounts of data at the typically resource-starved sensor nodes. This project develops efficient feature extraction and dimensionality reduction tools for decentralized sensing systems with minimal computation, storage and communication requirements of each sensor node to make sense of the surrounding dynamic environments. Students on this program will develop multi-disciplinary expertise in signal processing, machine learning, optimization, and statistics. New graduate-level courses on high-dimensional data analysis will be developed by the PI at Ohio State University. More specifically, this project offers an integrated approach for subspace learning from bits, where the sampling strategy explicitly accounts for the communication burden by only requesting a single bit from each sensor node. This project opens up opportunities to develop a theory of principal component analysis (or subspace learning) based on binary sensing, where noisy data samples are synthesized into coarse yet high-fidelity binary measurements that are more amenable for communication and inference. The consideration of binary measurements is well-motivated, as in practice, measurements are either mapped to bits from a finite alphabet before computation, or available naturally in the quantized form, such as comparison outcomes from human as sensors; constraints in storage and communication are often expressed in terms of the number of bits rather than the number of real measurements; finally, binary measurements are also more robust against unknown, nonlinear and heterogeneous distortions from different sensors compared with real measurements. Unfortunately, none of the existing subspace learning frameworks is tailored to acquire and process quantized measurements, and will yield highly sub-optimal results if naive quantization is applied. This project addresses the above challenge and highlights a novel interplay between the quantity, precision, and fidelity of measurements in sensing for estimating and tracking a low-dimensional subspace in a dynamic environment. Decentralized and online inference algorithms for subspace learning are developed together with adaptive sensing schemes to speed up convergence.

分散式感知系统在日常生活中发挥着越来越重要的作用，包括无线传感器网络、物联网移动人群感知、人工众包等，在网络分析、分布式宽带频谱感知、目标跟踪、环境监测和广告预测等方面都有应用。然而，尽管前景看好，但由于在通常资源匮乏的传感器节点上处理大量数据，有效的推理是极其具有挑战性的。该项目为分布式传感系统开发了高效的特征提取和降维工具，每个传感器节点只需最少的计算、存储和通信需求就可以理解周围的动态环境。这个项目的学生将在信号处理、机器学习、优化和统计方面发展多学科的专业知识。俄亥俄州立大学的PI将开发新的研究生级别的高维数据分析课程。更具体地说，该项目提供了一种从子空间比特学习的集成方法，其中采样策略通过只向每个传感器节点请求单个比特来显式地考虑通信负担。该项目为发展基于二进制传感的主成分分析(或子空间学习)理论提供了机会，在该理论中，噪声数据样本被合成成更适于通信和推理的粗略但高保真的二进制测量。二进制测量的考虑是出于良好的动机，因为在实践中，测量要么在计算之前被映射到有限字母表中的比特，要么自然地以量化的形式获得，例如来自人类作为传感器的比较结果；存储和通信中的约束通常以比特数而不是真实测量的数量来表示；最后，与真实测量相比，二进制测量对于来自不同传感器的未知、非线性和异质失真也具有更强的鲁棒性。不幸的是，现有的子空间学习框架都不是为获取和处理量化测量而量身定做的，如果应用朴素量化，将产生高度次优的结果。该项目解决了上述挑战，并突出了动态环境中用于估计和跟踪低维子空间的传感中测量的数量、精度和保真度之间的新的相互作用。子空间学习的分布式和在线推理算法与自适应感知方案相结合，以加快收敛速度。