Tensors for Data Processing: Theory, Methods, and Applications
Author(s): Yipeng Liu
Publisher: Academic Press
Publication Date: 1 Nov. 2021
Edition: 1st
Language: English
Print length: 596 pages
ISBN-10: 012824447X
ISBN-13: 9780128244470
Book Description
Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods.
As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry.
Provides a complete reference on classical and state-of-the-art tensor-based methods for data processing
Includes a wide range of applications from different disciplines
Gives guidance for their application
Editorial Reviews
Review
A complete reference of classical and state-of-the-art tensor methods and their applications
From the Back Cover
As a higher-order generalization of a matrix, a tensor is a natural representation for multi-dimensional data; tensor based processing can avoid multi-linear data structure loss that occurs in classical matrix based data processing methods. The move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics, quantum chemistry.
Tensors for Data Processing: presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing.
This reference is ideal for students, researchers, industry developers who want to understand and use tensor based data processing theories and methods.
About the Author
Yipeng Liu received the BSc degree in biomedical engineering and the PhD degree in information and communication engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, in 2006 and 2011, respectively. From 2011 to 2014, he was a postdoctoral research fellow at University of Leuven, Leuven, Belgium. Since 2014, he has been an associate professor with UESTC, Chengdu, China. His research interest is tensor signal processing. He has authored or co-authored over 70 publication, inculding a series of papers on sparse tensor, tensor completion, tensor PCA, tensor regression, and so on.
He has served as an associate editor for IEEE Signal Processing Letters (2019 – now), an editorial board member for Heliyon (2018 – 2019), and the managing guest editor for the special issue “tensor image processing in Signal Processing: Image Communication. He has served on technical or program committees for 5 international conferences. He is an IEEE senior member, a member of the Multimedia Technology Technical Committee of Chinese Computer Federation, and a member of China Society of Image and Graphics on Youth Working Committee.
He has given give tutorials for a few international conferences, including 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Workshop on Signal Processing Systems (SiPS), and 11th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), and is going to give tutorials on the 27th IEEE International Conference on Image Processing (ICIP 2020) and The 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020). He has been teaching optimization theory and applications for graduates since 2015, and received the 8th University Teaching Achievement Award in 2016.
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