EAGER-DynamicData: Subspace Learning From Binary Sensing

分布式传感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，物联网的移动的人群传感，以及人类工作者的众包，应用于网络分析，分布式宽带频谱传感，目标跟踪，环境监测和广告预测。尽管有希望，但是，由于在通常资源匮乏的传感器节点处处理大量数据，高效的推断是极具挑战性的。该项目为分散式传感系统开发了有效的特征提取和降维工具，每个传感器节点的计算，存储和通信要求最低，以了解周围的动态环境。该计划的学生将发展信号处理，机器学习，优化和统计学方面的多学科专业知识。俄亥俄州州立大学的PI将开发关于高维数据分析的新的研究生课程。更具体地说，该项目提供了一个集成的方法，从比特子空间学习，采样策略明确占通信负担，只要求一个单一的位从每个传感器节点。该项目为开发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，其中噪声数据样本被合成为粗糙但高保真的二进制测量，更适合通信和推理。二进制测量的考虑是有动机的，因为在实践中，测量要么在计算之前被映射到来自有限字母表的比特，要么以量化形式自然可用，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的测量的数量来表示;最后，与真实的测量值相比，二进制测量值对于来自不同传感器的未知、非线性和异质失真也更鲁棒。不幸的是，没有一个现有的子空间学习框架被定制为获取和处理量化的测量，并且如果应用朴素量化，则将产生高度次优的结果。该项目解决了上述挑战，并强调了在动态环境中估计和跟踪低维子空间的传感测量的数量，精度和保真度之间的新的相互作用。子空间学习的分散和在线推理算法与自适应传感方案一起开发，以加快收敛速度。

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.