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Record Nr. |
UNINA9910483817003321 |
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Titolo |
Latent variable analysis and signal separation : 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010 : proceedings / / Vincent Vigneron ... [et al.] (eds.) |
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Pubbl/distr/stampa |
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Berlin ; ; New York, : Springer, 2010 |
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ISBN |
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1-280-38934-6 |
9786613567260 |
3-642-15995-8 |
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Edizione |
[1st ed. 2010.] |
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Descrizione fisica |
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1 online resource (XVIII, 655 p. 182 illus.) |
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Collana |
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LNCS sublibrary. SL 1, Theoretical computer science and general issues |
Lecture notes in computer science, , 0302-9743 ; ; 6365 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Latent variables |
Latent structure analysis |
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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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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Speech and Audio Applications -- Convolutive Signal Separation -- The 2010 Signal Separation Evaluation Campaign (SiSEC2010) -- Audio -- Theory -- Telecom -- Tensor Factorizations -- Sparsity I -- Sparsity; Biomedical Applications -- Non-negativity; Image Processing Applications -- Tensors; Joint Diagonalization -- Sparsity II -- Biomedical Applications -- Emerging Topics. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2010, held in St. Malo, France, in September 2010. The 25 papers presented were carefully reviewed and selected from over hundred submissions. The papers collected in this volume demonstrate that the research activity in the field continues to gather theoreticians and practitioners, with contributions ranging range from abstract concepts to the most concrete and applicable questions and considerations. Speech and audio, as well as biomedical applications, continue to carry the mass of the considered applications. Unsurprisingly the concepts of sparsity and non-negativity, as well as tensor decompositions, have become |
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predominant, reflecting the strong activity on these themes in signal and image processing at large. |
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