From Nistor.Grozavu at lipn.univ-paris13.fr  Wed Nov 23 11:53:41 2011
From: Nistor.Grozavu at lipn.univ-paris13.fr (Nistor Grozavu)
Date: Wed, 23 Nov 2011 11:53:41 +0100
Subject: [Nmf-commits] CFP: 2012 IEEE WCCI Special Session on Nonnegative
 Matrix factorization paradigm for unsupervised learning
Message-ID: <4ECCD0B5.8030506@lipn.univ-paris13.fr>

Dear Colleague,

I would like to cordially invite you to submit a paper for the special 
session on "Nonnegative Matrix factorization paradigm for unsupervised 
learning 
<http://www-lipn.univ-paris13.fr/%7Egrozavu/IJCNN/default.html>" 
(http://www-lipn.univ-paris13.fr/~grozavu/IJCNN/default.html)
organized within the 2012 IEEE World Congress on Computational 
Intelligence  ( IJCNN)  which will be held on June 10-15, 2012, in 
Brisbane, Australia.

*Paper submission deadline : *Dec 19, 2011
For the paper submission guidlines please refer to the WCCI website 
<http://www.ieee-wcci2012.org/ieee-wcci2012/>.


  Introduction to the Special Session
  <http://www-lipn.univ-paris13.fr/%7Egrozavu/IJCNN/default.html>:

This special session will cover original and pioneering contributions, 
theory as well as applications on nonnegative matrix factorization (NMF) 
paradigm for unsupervised learning, and aim at an inspiring discussion 
on the recent progress and the future development.
A fundamental problem in many machine learning tasks is to find a 
suitable representation of the data. A useful representation typically 
makes latent structure in the data explicit, and often reduces the 
dimensionality of the data so that further computational methods can be 
applied.
NMF is a commonly used approach to understanding the latent structure of 
the observed matrix for various applications. NMF methods have attracted 
increasing attention in recent years because of their mathematical 
elegance and encouraging empirical results.
There are many forms of NMF. Previous work has shown that by respecting 
the nonnegativity, the factorization results will be easier to interpret 
while being comparable to, or better than, other techniques like SVD on 
effectiveness NMF has been successfully applied to a variety of 
applications, including face detection and recognition, audio and speech 
processing, text mining, biomedical image analysis, bioinformatics, and 
so on.

In this special session, the main methods of matrix factorization 
paradigm for unsupervised learning will be presented. Also, the 
effectiveness of these methods will be discussed considering the 
concepts of diversity and selection of these approaches.

Topics of interest include but not limited to:
     - Convex-NMF
        - Hard clustering and NMF
        - Kernel-NMF
        - NMF for Large-Scale Data
        - Maximum margin matrix factorization (MMMF)
        - NMF with Sparseness Constraints
        - Orthogonal symmetric NMF
        - Probabilistic NMF
        - Relaxed NMF
        - Semi-NMF
        - Tri-NMF
        - Weighted NMF
        - Weighted NMTri-Factorization
        -  Dimensionality reduction via  matrix factorization

Organizers:
*Youn?s BENNANI, Full Professor - Paris 13 University
**Nistor GROZAVU, Associate Professor - Paris 13 University
**Mohamed NADIF, Full Professor - Paris Descartes University
**Nicoleta ROGOVSCHI, Associate Professor - Paris Descartes University*
Best regards,
Nistor Grozavu
PhD, Computer Science Laboratory of the Paris 13 University (LIPN)
http://www-lipn.univ-paris13.fr/~grozavu/
tel: +33 (0)626901790
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