EAGER-DynamicData: Subspace Learning From Binary Sensing

EAGER-DynamicData:从二进制感知中学习子空间

基本信息

  • 批准号:
    1833553
  • 负责人:
  • 金额:
    $ 8.18万
  • 依托单位:
  • 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

项目摘要

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。

项目成果

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Yuejie Chi其他文献

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
解决基于模型的离线强化学习的样本复杂度
  • DOI:
    10.48550/arxiv.2204.05275
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gen Li;Laixi Shi;Yuxin Chen;Yuejie Chi;Yuting Wei
  • 通讯作者:
    Yuting Wei
Memory-Limited stochastic approximation for poisson subspace tracking
泊松子空间跟踪的内存有限随机近似
Principal subspace estimation for low-rank Toeplitz covariance matrices with binary sensing
具有二元感知的低秩 Toeplitz 协方差矩阵的主子空间估计
Regularized blind detection for MIMO communications
MIMO 通信的正则盲检测
Golay complementary waveforms for sparse delay-Doppler radar imaging
用于稀疏延迟多普勒雷达成像的 Golay 互补波形

Yuejie Chi的其他文献

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{{ truncateString('Yuejie Chi', 18)}}的其他基金

Federated Optimization over Bandwidth-Limited Heterogeneous Networks
带宽受限异构网络的联合优化
  • 批准号:
    2318441
  • 财政年份:
    2023
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
Collaborative Research: Towards a Theoretic Foundation for Optimal Deep Graph Learning
协作研究:为最优深度图学习奠定理论基础
  • 批准号:
    2134080
  • 财政年份:
    2022
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Continuing Grant
NSF Student Travel Grant for the Fifth Conference on Machine Learning and Systems (MLSys 2022)
第五届机器学习和系统会议 (MLSys 2022) 的 NSF 学生旅费补助金
  • 批准号:
    2219655
  • 财政年份:
    2022
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Medium: Statistical and Algorithmic Foundations of Efficient Reinforcement Learning
合作研究:CIF:媒介:高效强化学习的统计和算法基础
  • 批准号:
    2106778
  • 财政年份:
    2021
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Continuing Grant
Taming Nonlinear Inverse Problems: Theory and Algorithms
驯服非线性反问题:理论与算法
  • 批准号:
    2126634
  • 财政年份:
    2021
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
CIF: Small: Resource-Efficient Statistical Inference in Networked Environments
CIF:小型:网络环境中资源高效的统计推断
  • 批准号:
    2007911
  • 财政年份:
    2020
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
CIF: Medium: Collaborative Research: Theory of Optimization Geometry and Algorithms for Neural Networks
CIF:媒介:协作研究:神经网络优化几何理论和算法
  • 批准号:
    1901199
  • 财政年份:
    2019
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
CIF: Small: Inverse Methods for Parametric Mixture Models
CIF:小:参数混合模型的逆方法
  • 批准号:
    1826519
  • 财政年份:
    2018
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant
CIF: Medium: Collaborative Research: Nonconvex Optimization for High-Dimensional Signal Estimation: Theory and Fast Algorithms
CIF:中:协作研究:高维信号估计的非凸优化:理论和快速算法
  • 批准号:
    1806154
  • 财政年份:
    2018
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Continuing Grant
CAREER: Robust Methods for High-Dimensional Signal Processing under Geometric Constraints
职业:几何约束下高维信号处理的鲁棒方法
  • 批准号:
    1818571
  • 财政年份:
    2018
  • 资助金额:
    $ 8.18万
  • 项目类别:
    Standard Grant

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EAGER-DynamicData: Generative Statistical Modeling for Dynamic and Distributed Data
EAGER-DynamicData:动态和分布式数据的生成统计建模
  • 批准号:
    1462230
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