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<div>Dear Colleague,<br>
<br>
I would like
to cordially invite you to submit a paper for the special session on
"<a rel="nofollow" target="_blank"
href="http://www-lipn.univ-paris13.fr/%7Egrozavu/IJCNN/default.html">Nonnegative
Matrix factorization paradigm for unsupervised learning</a>"
(<a class="moz-txt-link-freetext" href="http://www-lipn.univ-paris13.fr/~grozavu/IJCNN/default.html">http://www-lipn.univ-paris13.fr/~grozavu/IJCNN/default.html</a>)<br>
</div>
<div>organized
within
the 2012 IEEE World Congress on Computational Intelligence ( IJCNN)
which will be held on June 10-15, 2012, in Brisbane, Australia.</div>
<br>
<div> <b>Paper submission deadline : </b><span
style="color: rgb(255, 0, 0);">Dec 19, 2011</span></div>
For the paper submission guidlines please refer to <a rel="nofollow"
target="_blank" href="http://www.ieee-wcci2012.org/ieee-wcci2012/">the
WCCI website</a>.
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<h1 class="yiv1097185613western">Introduction to the <a rel="nofollow"
target="_blank"
href="http://www-lipn.univ-paris13.fr/%7Egrozavu/IJCNN/default.html">Special
Session</a>:</h1>
<div style="margin-bottom: 0cm;"> 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.<br>
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.<br>
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.<br>
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.<br>
<br>
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.<br>
<br>
Topics of interest include but not limited
to:<br>
- Convex-NMF<br>
-
Hard clustering and NMF<br>
- Kernel-NMF<br>
- NMF for Large-Scale Data<br>
- Maximum margin matrix factorization (MMMF)<br>
- NMF with Sparseness Constraints<br>
-
Orthogonal symmetric NMF<br>
-
Probabilistic NMF<br>
- Relaxed NMF<br>
- Semi-NMF<br>
-
Tri-NMF<br>
- Weighted NMF<br>
- Weighted NMTri-Factorization<font size="1"><font size="2"><br>
<big><big> - Dimensionality reduction via matrix factorization </big></big></font></font><br>
</div>
<br>
Organizers:<br>
<b>Younès BENNANI, Full Professor - Paris 13 University<br>
</b><b>Nistor GROZAVU, Associate Professor - Paris 13 University<br>
</b><b>Mohamed NADIF, Full Professor - Paris Descartes University<br>
</b><b>Nicoleta ROGOVSCHI, Associate Professor - Paris Descartes
University</b></div>
<div> </div>
<div>Best regards,<br>
Nistor Grozavu<br>
PhD, Computer Science Laboratory of the Paris 13 University (LIPN)<br>
<a class="moz-txt-link-freetext" href="http://www-lipn.univ-paris13.fr/~grozavu/">http://www-lipn.univ-paris13.fr/~grozavu/</a><br>
tel: +33 (0)626901790</div>
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