LVA/ICA: Latent Variable Analysis and Signal Separation

FacebookTwitterLinkedInGoogle

 

Past:   Proceedings on DBLP

Future:  Post a CFP for 2011 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
LVA/ICA 2010 International Conference on Latent Variable Analysis and Signal Separation
Sep 27, 2010 - Sep 30, 2010 Saint Malo, France Apr 16, 2010
 
 

Present CFP : 2010

Ten years after the first ICA workshop in Aussois (France), the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this field. While ICA and blind signal separation have become mainstream topics, new approaches have emerged to solve problems involving signal mixtures or various other types of latent variables: semi-blind models, matrix factorization using SCA, NMF, PLSI, but also tensor decompositions, IVA, ISA, ... The 9th edition of the conference, renamed LVA/ICA to reflect this evolution towards more general Latent Variable Analysis problems in signal processing, will offer an interdisciplinary forum for scientists and engineers to experience renewed theoretical surprises and face real-world problems. In addition to contributed papers, the meeting will feature keynote talks by leading researchers (P. Comon, S. Mallat, M. Girolami, A. Yeredor), a community-based evaluation campaign (SiSEC 2010), a panel discussion session, and a special late-breaking / demo session.

Prospective authors are invited to submit papers in all areas of latent variable analysis and signal separation, including but not limited to:

* Theoretical frameworks: probabilistic, geometric & biologically-inspired modeling; flat, hierarchical & dynamic structures; sparse coding; kernel methods; neural networks
* Models: linear & nonlinear models; continuous & discrete latent variables; convolutive & noisy mixtures; linear & quadratic time-frequency representations
* Algorithms: blind & semi-blind estimation; identification & convergence conditions; local & evolutionary optimization; computational complexity; adaptation & modularity
* Speech and audio data: source separation; denoising & dereverberation; CASA; ASR
* Images: segmentation; fusion; texture analysis; color imaging; coding; scene analysis
* Biomedical data: functional imaging; BCI; genomic data analysis; systems biology
* Unsolved and emerging problems: causality detection; feature selection; data mining; control; psychology; social networks; finance; artificial intelligence; real-time applications
* Resources: software; databases; objective & subjective evaluation procedures

ADDITIONAL INFORMATION

Papers must be original and must not be already published nor under review elsewhere. Papers linked to a submission to SiSEC 2010 are highly welcome. The proceedings will be published in Springer-Verlag's Lecture Notes in Computer Science (LNCS) Series. Extended versions of selected papers will be considered for a special issue of a journal. The best student paper will be distinguished by an award.

IMPORTANT DATES

April 16, 2010: Extended paper submission deadline (Former deadline: April 7, 2010)
June 15, 2010: Notification of acceptance
June 30, 2010: Deadline for submitting camera-ready papers
July 31, 2010: Late-breaking/demo/SiSEC abstract submission deadline
September 27-30, 2010: Conference dates
 

Related Resources

DSA 2019   The Frontiers in Intelligent Data and Signal Analysis DSA 2019
CVPR 2019   Computer Vision and Pattern Recognition
IPMI 2019   Information Processing in Medical Imaging
ICCV 2019   International Conference on Computer Vision
CAIP 2019   Computer Analysis of Images and Patterns
ISMM 2019   International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
ICDAR 2019   International Conference on Document Analysis and Recognition
ICML 2019   36th International Conference on Machine Learning