Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends(r) in Machine Learning)

Cichocki, Andrzej; Lee, Namgil; Oseledets, Ivan; Phan, Anh-Huy; Zhao, Qibin; Mandic, Danilo P

ISBN 10: 168083276X / ISBN 13: 9781680832761

Edité par Now Publishers, 2017

N° de réf. du vendeur ABLIING23Mar2811580338172

Synopsis :

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 computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable largescale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions.

Détails bibliographiques

Titre : Tensor Networks for Dimensionality Reduction...
Éditeur : Now Publishers
Date d'édition : 2017
Reliure : Couverture souple
Etat : New