NEWS
Paper Submission Deadline has been extended to Jan 18, 2012 (23:59 EST) - The Paper Submission System will close straight after that, no more extensions will be given.AIMS AND SCOPE
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