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Tensors for Data Processing: Theory, Methods, and Applications

LeeAndro

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Trusted Editor
Tensors for Data Processing: Theory, Methods, and Applications
English | 2021 | ISBN: ‎ 012824447X | 598 pages | pdf, epub | 79.79 MB

presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and eeering 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 eeering, 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





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