Tensors for Data Processing 1st Edition Yipeng Liu

Tensors for Data Processing – Ebook Instant Download/Delivery ISBN(s): 9780128244470,012824447X

Product details:

• ISBN-10 ‏ : ‎ 012824447X
• ISBN-13 ‏ : ‎ 978-0128244470
• Author(s): Yipeng Liu

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.

Table contents:

Chapter 1: Tensor decompositions: computations, applications, and challenges
Chapter 2: Transform-based tensor singular value decomposition in multidimensional image recovery
Chapter 3: Partensor
Chapter 4: A Riemannian approach to low-rank tensor learning
Chapter 5: Generalized thresholding for low-rank tensor recovery: approaches based on model and learning
Chapter 6: Tensor principal component analysis
Chapter 7: Tensors for deep learning theory
Chapter 8: Tensor network algorithms for image classification
Chapter 9: High-performance tensor decompositions for compressing and accelerating deep neural networks
Chapter 10: Coupled tensor decompositions for data fusion
Chapter 11: Tensor methods for low-level vision
Chapter 12: Tensors for neuroimaging
Chapter 13: Tensor representation for remote sensing images
Chapter 14: Structured tensor train decomposition for speeding up kernel-based learning
References
Index

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