Distributed Graph-based Semi-supervised Classifiers: Sampling and Interpolation

基于分布式图的半监督分类器：采样和插值

• 批准号: 551992-2020
• 负责人: Cheung, Gene
• 金额: $ 1.99万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721279
• 关键词: Distributed; Graph; based; Semi; supervised

We study distributed classifier learning from a graph signal processing (GSP) perspective. In practical networked systems, big data often reside in geographically diverse regions scattered in local networks around the world. Leveraging on Cisco's mature and extensive network infrastructure and our in-house expertise in GSP, we develop an efficient distributed graph-based classifier learning framework, by extracting and disseminating knowledge from data located across a large network. Specifically, we focus on two core problems in graph-based semi-supervised classifier learning: distributed graph sampling and distributed signal interpolation. Distributed graph sampling is the problem of pre-selecting nodes on the graph based solely on local network information to acquire informative labels. Distributed graph interpolation is the problem of interpolating missing samples in the remaining nodes, given a set of observed labels in a local neighborhood. We approach both problems from a graph spectral perspective, resulting in a classifier system with theoretically derived and explainable performance guarantees.

我们从图形信号处理（GSP）的角度研究分布式分类器学习。在实际的网络系统中，大数据通常驻留在分散在世界各地的本地网络中的地理上不同的区域中。利用思科成熟和广泛的网络基础设施以及我们在GSP方面的内部专业知识，我们通过从位于大型网络中的数据中提取和传播知识，开发了一个高效的分布式基于图的分类器学习框架。具体而言，我们专注于基于图的半监督分类器学习的两个核心问题：分布式图采样和分布式信号插值。分布式图抽样是指仅根据局部网络信息预先选择图上的节点以获取信息标签的问题。分布式图插值是在给定局部邻域中的一组观测标签的情况下，在剩余节点中插值缺失样本的问题。我们从图谱的角度来处理这两个问题，从而在理论上推导和解释的性能保证分类系统。