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

Editore: Springer International Publishing, Springer International Publishing, 2018

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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.

Codice articolo 9783319937632

Dati bibliografici

• Titolo: Latent Variable Analysis and Signal Separation : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2¿5, 2018, Proceedings
• Editore: Springer International Publishing, Springer International Publishing
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319937634
• ISBN 13: 9783319937632
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 33 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 33 millimetri di profondità
• Peso: 896 grammi

Descrizione articolo

Riassunto

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.