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.

分散的传感系统在日常生活中起着越来越重要的作用，包括无线传感器网络，带有文字的移动人群敏感以及与人类工人的众包，并在网络分析中应用，分布式宽带光谱感应，目标跟踪，环境监测和广告预测。但是，尽管有希望，但由于在通常的资源含有的传感器节点上处理大量数据，因此有效的推断极具挑战性。该项目为分散的传感系统开发了有效的功能提取和降低降低工具，其计算，存储和通信要求最小，以使周围的动态环境有意义。该计划的学生将在信号处理，机器学习，优化和统计数据方面发展多学科专业知识。俄亥俄州立大学的PI将开发有关高维数据分析的新的研究生级课程。更具体地说，该项目提供了一种从位学习的集成方法，用于从位学习，在该方法中，采样策略仅通过要求每个传感器节点的单个位来明确说明通信负担。该项目为基于二进制传感开发主要组件​​分析理论（或子空间学习）开辟了机会，在该理论中，将嘈杂的数据样本合成为粗糙但高的二进制二进制测量结果，这些测量更适合通信和推理。考虑到二进制测量值的考虑是有充分动机的，因为在实践中，测量值要么在计算前映射到有限字母中的位，要么以量化形式自然使用，例如人类作为传感器的比较结果；存储和通信中的约束通常是根据位数而不是实际测量数来表示的。最后，与实际测量值相比，不同传感器的未知，非线性和异质变形也更加健壮。不幸的是，现有的子空间学习框架均未量身定制用于获取和处理量化的测量结果，如果应用天真的量化，将产生高度优化的结果。该项目解决了上述挑战，并突出了测量的数量，精度和忠诚度之间的新型相互作用，以感知在动态环境中估算和跟踪低维度子空间。开发了用于子空间学习的分散和在线推理算法以及自适应传感方案，以加快融合的速度。