Latent Variable Analysis and Signal Separation (Paperback)

Yannick Deville

ISBN 10: 3319937634 / ISBN 13: 9783319937632

Published by Springer International Publishing AG, Cham, 2018

About this Item

Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783319937632

Bibliographic Details

• Title: Latent Variable Analysis and Signal Separation (Paperback)
• Publisher: Springer International Publishing AG, Cham
• Publication Date: 2018
• Language: English
• ISBN 10: 3319937634
• ISBN 13: 9783319937632
• Binding: Paperback
• Condition: new

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.