1 option
Latent Variable Analysis and Signal Separation : 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.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 10891
- Theoretical Computer Science and General Issues, 2512-2029 ; 10891
- Language:
- English
- Subjects (All):
- 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.
- Local Subjects:
- Automated Pattern Recognition.
- Computer Vision.
- Artificial Intelligence.
- Computer Modelling.
- Numerical Analysis.
- Computer Communication Networks.
- Physical Description:
- 1 online resource (XVII, 580 pages) : 150 illustrations
- Edition:
- 1st ed. 2018.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2018.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-93764-9
- 9783319937649
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.