Statistical Signal Processing and Learning on Networks and Graphs

网络和图的统计信号处理和学习

• 批准号: RGPIN-2020-04661
• 负责人: Zhang, XiaoPing
• 金额: $ 3.35万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717242
• 关键词: Statistical; Signal; Processing; Learning; Networks

Signal processing along with the resurgent AI technology have become ubiquitous to process data from all types of sources. Many data, either from networked sources such as sensor networks and social networks or from other sources such as images and videos, contain explicit or implicit structured relationships that can be best represented by a graph. These types of network data pose new challenges to signal processing research. Recent signal processing and statistical modeling technologies for data with a graph structure mainly include a) graphical modeling and learning theory, and b) graph signal processing. However, these theories and methods have not been fully investigated under various constraints posed by emerging applications to solve problems such as localization and tracking, source identification, optimal filtering over networks. In addition, in many cases, identification or learning of the unknown or implicit network/graph structures is a very challenging problem. The overall objective of the proposed research is to systematically develop new signal and data processing theories and methods for data that have explicit, such as in sensor networks, or implicit, such as in videos, underlying nonlocal graph based relationships and/or dynamic information diffusion flow, and applications in sensor/social networks, 5G Internet of things (IoT) wireless networks, multimodality multimedia content analysis, and economic big data. Specific objectives are (i) to develop general network statistical signal processing and inference models and methods over a sensor network for dynamic multiple target localization and tracking; (ii) to identify original signal sources in the presence of noise and interference for data generated by a network information diffusion process, based on the graph shift operator theory, and to develop optimal filtering and prediction methods over the network; also to develop the optimal learning algorithms to estimate the network structure if it is unknown; and (iii) to design a graph shift operator or manifold kernel that can best represent the complex nonlocal hidden correlation and statistical structures of data for better data filtering, analysis and processing. We will further apply our research to applications such as multimedia signal processing, video event detection and predictions, 5G IoT networks, social networks, finance and economic big data analysis.

信号处理以及复兴的AI技术已无处不在，可以从所有类型的来源处理数据。许多数据，来自传感器网络和社交网络等网络来源，或者来自图像和视频等其他来源，都包含可以最好由图表表示的明确或隐式结构关系。这些类型的网络数据提出了新的挑战，可以发出加工研究。具有图形结构的数据的最新信号处理和统计建模技术主要包括a）图形建模和学习理论以及b）图形信号处理。但是，这些理论和方法尚未在新兴应用程序构成的各种限制下得到充分研究，以解决诸如本地化和跟踪，源识别，对网络上的最佳过滤等问题。此外，在许多情况下，对未知或隐式网络/图形结构的识别或学习是一个非常具有挑战性的问题。 The overall objective of the proposed research is to systematically develop new signal and data processing theories and methods for data that have explicit, such as in sensor networks, or implicit, such as in videos, underlying nonlocal graph based relationships and/or dynamic information diffusion flow, and applications in sensor/social networks, 5G Internet of things (IoT) wireless networks, multimodality multimedia content analysis, and economic big data. 特定的目标是（i）在传感器网络上开发一般网络统计信号处理以及推理模型和方法，以动态多重目标定位和跟踪； （ii）基于图形移动操作员理论，在存在噪声和网络信息扩散过程中的噪声和干扰的情况下确定原始信号源，并在网络上开发最佳的过滤和预测方法；还要开发最佳学习算法以估计网络结构是否未知； （iii）设计图形移位操作员或歧管内核，该内核最能代表数据的复杂非局部隐藏相关性和数据的统计结构，以进行更好的数据过滤，分析和处理。我们将进一步将研究应用于多媒体信号处理，视频事件检测和预测，5G IoT网络，社交网络，金融和经济大数据分析等应用程序。