Frame Theory in Data Science

ISBN-13: 9783031494826 / Angielski

Zhihua Zhang; Palle E. T. Jorgensen

Spis treści

Abstract Frame Theory.- Fourier-type Frame Theory.- Bandlimited Framelet Theory.- Compactly Supported Framelet Theory.- Periodic Framelet Theory.- Spheroidal-type Frame Theory.- Big Data.- Climate Diagnosis.- Frame Neural Networks.

O autorze

Zhihua Zhang is a Taishan Distinguished Professor at Shandong University, China and is leading an interdisciplinary big data mining research group. His long-standing researches focus on big data, climate change mechanisms, environmental evolution and sustainability. He has published 8 books (2 with Elsevier, 5 with Springer, and 1 with DeGruyter) and more than 70 articles. He is a chief editor, associate editor, or editorial board member of many global known journals on applied mathematics, climate and environmental science, as well as the first-track chair and plenary speaker of Mediterranean Geosciences Union Annual Meeting.

Palle E.T. Jorgensen is a Professor at the University of Iowa. His prior academic/teaching positions include the University of Pennsylvania, Stanford University, and Aarhus University (Denmark.) He has authored more than 300 highly cited research papers, and more than 10 books. He has received numerous honors and awards, including in 2018 Jorgensen being the NSF/CBMS speaker, giving 10 lectures; titled Harmonic Analysis: Smooth and Non-smooth, published as vol. 128, in the AMS/CBMS book series. He is a frequent invited speaker, giving colloquium and conference presentations at universities in the US, and around the World. 

Blurb

This book establishes brand-new frame theory and technical implementation in data science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis. Given that data science is unanimously recognized as a core driver for achieving Sustainable Development Goals of the United Nations, these frame techniques bring fundamental changes to multi-channel data mining systems and support the development of digital Earth platforms. This book integrates the authors' frame research in the past twenty years and provides cutting-edge techniques and depth for scientists, professionals, and graduate students in data science, applied mathematics, environmental science, and geoscience.