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Record Nr. |
UNINA9910349433403321 |
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Titolo |
Latent Variable Analysis and Signal Separation [[electronic resource] ] : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2–5, 2018, Proceedings / / edited by Yannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XVII, 580 p. 150 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 10891 |
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Disciplina |
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Soggetti |
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Pattern recognition systems |
Computer vision |
Artificial intelligence |
Computer simulation |
Numerical analysis |
Computer networks |
Automated Pattern Recognition |
Computer Vision |
Artificial Intelligence |
Computer Modelling |
Numerical Analysis |
Computer Communication Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 14th International |
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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. |
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