Tensor decomposition is the process of breaking down a tensor into a sum of simpler components, making it easier to analyze and compute with high-dimensional data. This technique allows for the extraction of latent structures and patterns from tensors, enabling efficient representation and manipulation in applications like signal processing, data mining, and machine learning. By reducing the complexity of multi-dimensional arrays, tensor decomposition facilitates operations like tensor-matrix products and enhances computational efficiency.
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