Latent Variable Analysis and Signal Separation 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2-5, 2018, Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Deville, Yannick (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Gannot, Sharon (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Mason, Russell (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Plumbley, Mark D. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Ward, Dominic (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Theoretical Computer Science and General Issues ; 10891
• Θέματα: Pattern recognition.; Optical data processing.; Artificial intelligence.; Computer simulation.; Numerical analysis.; Computer communication systems.; Pattern Recognition.; Image Processing and Computer Vision.; Artificial Intelligence.; Simulation and Modeling.; Numeric Computing.; Computer Communication Networks.

 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. 

 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.