Tensor Networks for Dimensionality Reduction and Large-scale Optimization

Tensor Networks for Dimensionality Reduction and Large-scale Optimization

Part 2 Applications and Future Perspectives

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This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computati...