Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
Graduate students and researchers in computer science and engineering
1. Introduction to tensor computation
2. Tensor factorizations
3. Compressive sensing of tensor
4. Tensor completion
5. Coupled tensor for data fusion
6. Support tensor machine
7. Multilinear discriminative component analysis
8. Neural tensor networks
9. Multilinear independent component analysis
10. Multilinear principal component analysis
11. Linked tensor component analysis
12. Multilinear subspace clustering
13. Tensor for denoising
14. Tensor for feature extraction
15. Tensor for signal’s quality assessment
16. Tensor for image segmentation
17. Tensor for image registration
18. Tensor for wireless communication
19. Tensor for radar
20. Tensor for medical imaging
21. Tensor for speech and audio processing
22. Tensor for biomedical monitoring
- No. of pages:
- © Academic Press 2021
- 1st November 2021
- Academic Press
- Paperback ISBN:
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.
Associate Professor, UESTC, Chengdu, China
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.