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

Name: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends in Machine Learning)
Brand: Now Publishers
Price: 82.5 GBP
Availability: OutOfStock

Computer Science

Price History & Forecast

Last 31 days • 31 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 31 days • 1 price levels

Days at Price

Price Analysis

Most common price: £83 (31 days, 100.0%)

Price range: £83 - £83

Price levels: 1 different prices over 31 days

Description

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 large-scale 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. ISBN 978-1-68083-222-8

Product Specifications

Brand: Now Publishers
Format: Paperback
ASIN: 168083276X
Category: Books > Subjects > Computing & Internet > Computer Science
Domain: Amazon UK
Release Date: 30 May 2017
Listed Since: 08 June 2017

Barcode

No barcode data available

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

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (Foundations and Trends (R) in Machine Learning)

Tensor Regression (Foundations and Trends® in Machine Learning)

Tensor Computation for Data Analysis

Spectral Learning on Matrices and Tensors (Foundations and Trends® in Machine Learning)

Theory and Computation of Complex Tensors and its Applications

Linear Algebra and Optimization for Machine Learning: A Textbook

Elements of Dimensionality Reduction and Manifold Learning

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Machine Learning: A Bayesian and Optimization Perspective

Neural Networks and Deep Learning: A Textbook

Neural Networks and Deep Learning: A Textbook

Non-convex Optimization for Machine Learning (Foundations and Trends in Machine Learning)

Neural Networks and Deep Learning: A Textbook

Tensor Decompositions for Data Science

From Algebraic Structures to Tensors (Digital Signal and Image Processing: Matrices and Tensors in Signal Processing Set)

An Introduction to Neural Data Compression (Foundations and Trends® in Computer Graphics and Vision)

CRC Press Manifold Learning Theory and Applications Book

Optimization for Machine Learning (Neural Information Processing series)

Convex Optimization for Machine Learning (NowOpen)

Springer - Unsupervised Feature Extraction in Bioinformatics

Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends® in Machine Learning)

Parallelism in Matrix Computations (Scientific Computation)

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization