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: $ 3.06万
• 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: 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=703269
• 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.

我们从图信号处理(GSP)的角度研究分布式分类器学习。在实际的联网系统中，大数据通常驻留在分散在世界各地的本地网络中的不同地理区域中。利用思科成熟而广泛的网络基础设施和我们在GSP方面的内部专业知识，我们通过从大型网络中的数据中提取和传播知识，开发了一个高效的基于图形的分布式分类器学习框架。具体而言，我们重点研究了基于图的半监督分类器学习中的两个核心问题：分布式图采样和分布式信号内插。分布式图采样是指仅根据本地网络信息预先选择图上的节点以获得信息性标签的问题。分布式图内插是在给定局部邻域中一组观察到的标签的情况下，对剩余节点中丢失的样本进行内插的问题。我们从图谱的角度来处理这两个问题，从而得到一个具有理论推导和可解释的性能保证的分类器系统。