Latent Variable Analysis and Signal Separation: 14th International Conference, LVA/ICA 2018, Guildford,...

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Yannick Deville Sharon Gannot

Latent Variable Analysis and Signal Separation

14th International Conference, LVA/ICA 2018, Guildford, UK, July 2–5, 2018, Proceedings

Yannick Deville, Sharon Gannot, Russell Mason , Mark D. Plumbley

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English

Springer International Publishing AG
06 June 2018

3D graphics & modelling; Network hardware; Mathematical theory of computation; Pattern recognition; Image processing

Series: Lecture Notes in Computer Science

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.

Edited by: Yannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 2018 ed.
Volume: 10891
Dimensions: Height: 235mm, Width: 155mm,
Weight: 908g
ISBN: 9783319937632
ISBN 10: 3319937634
Series: Lecture Notes in Computer Science
Pages: 580
Publication Date: 06 June 2018
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

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.- Biomedical Data and Methods.