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As a higher-order generalization of a matrix, a tensor is a natural representation for multi-dimensional data; tensor based processing can avoid multi-linear data structure loss that occurs in classical matrix based data processing methods. The 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, quantum chemistry. Tensors for Data Processing: 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, industry developers who want to understand and use tensor based data processing theories and methods. A complete reference on classical and state-of-the-art tensor based methods for data processingA wide range of applications from different disciplinesGives guidance for their application
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