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Full 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.
Contents
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
