Tensors for Data Processing Theory, Methods, and Applications

Langue : Anglais

Coordonnateur : Liu Yipeng

Couverture de l’ouvrage Tensors for Data Processing

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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.

1 Tensor decompositions: computations, applications, and challenges

2 Transform-based tensor singular value decomposition in multidimensional image recovery

3 Partensor

4 A Riemannian approach to low-rank tensor learning

5 Generalized thresholding for low-rank tensor recovery: approaches based on model and learning

6 Tensor principal component analysis

7 Tensors for deep learning theory

8 Tensor network algorithms for image classification

9 High-performance tensor decompositions for compressing and accelerating deep neural networks

10 Coupled tensor decompositions for data fusion

11 Tensor methods for low-level vision

12 Tensors for neuroimaging

13 Tensor representation for remote sensing images

14 Structured tensor train decomposition for speeding up kernel-based learning

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.

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