Latent Variable Analysis and Signal Separation : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2¿5, 2018, Proceedings

Yannick Deville

ISBN 10: 3319937634 / ISBN 13: 9783319937632

Published by Springer International Publishing, Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA2018, held in Guildford, UK, in July 2018.The 52 full papers were carefully reviewed and selected from 62 initial submissions.As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.

Seller Inventory # 9783319937632

Bibliographic Details

• Title: Latent Variable Analysis and Signal Separation : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2¿5, 2018, Proceedings
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319937634
• ISBN 13: 9783319937632
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 33D millimeters 235 millimeters width by 155 millimeters height by 33 millimeters depth
• Weight: 896 grams

About this title

Synopsis

This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in  Guildford, UK, in July 2018.

The 52 full papers  were carefully reviewed and selected from 62 initial submissions. 

As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.