Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes - Zhao, Chunhui; Yu, Wanke
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Erscheint vorauss. 1. Juli 2025
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Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop…mehr

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Produktbeschreibung
Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models. The book not only discusses the complex models but also their real-world applications in industry.
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Autorenporträt
Chunhui Zhao is a Qiushi distinguished professor at Zhejiang University in China, and an expert in intelligent industrial monitoring with 20 years of experience in this field. She has authored or co-authored more than 400 papers in peer-reviewed international journals and conferences. Her research interests include statistical machine learning and data mining for industrial applications.