期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 168, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2022.108023
关键词
-
资金
- U.S. National Science Foundation (NSF) under BIGDATA [IIS-1837812]
This article explores the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices. SPD matrices, commonly used in chemical engineering and image analysis, can benefit from techniques that exploit the properties of Riemannian manifold in tasks such as classification and dimensionality reduction.
We explore the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices (SPD). An SPD matrix is a versatile data representation that is commonly used in chemical engineering (e.g., covariance/correlation/Hessian matrices and images) and powerful techniques are available for its analysis (e.g., principal component analysis). A key observation that motivates this work is that SPD matrices live on a Riemannian manifold and that implementing techniques that exploit this basic property can yield significant benefits in data-centric tasks such as classification and dimensionality reduction. We demonstrate this via a couple of case studies that conduct anomaly detection in the context of process monitoring and image analysis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据