CALL FOR PAPERS
IEEE Signal Processing Magazine
Special Issue on Dimensionality Reduction via Subspace and Manifold Learning
※Guest Editors:
Yi Ma - Department of Electrical and Computer Engineering, University of
Illinois at Urbana-Champaign, (yima@uiuc.edu)
Partha Niyogi - Department of Computer Sciences, Statistics, University of
Chicago, (niyogi@cs.uchicago.edu)
Guillermo Sapiro - Department of Electrical and Computer Engineering,
University of Minnesota, (guile@ece.umn.edu)
Rene Vidal - Department of Biomedical Engineering, Computer Sciences,
Johns Hopkins University, (rvidal@cis.jhu.edu)
The problem of finding and exploiting low-dimensional structures in
high-dimensional data is taking on increasing importance in image, video,
or audio processing, web data analysis/search, and bioinformatics, where
datasets now routinely lie in thousands to millions-dimensional
observation spaces. The curse of dimensionality is in full play here: We
often need to conduct meaningful inference with limited number of samples
in a very high-dimensional space. Conventional statistical and
computational tools are often severely inadequate for processing and
analyzing high-dimensional data. Although the data might be presented in
a high-dimensional space, their intrinsic complexity and local dimensions
are typically much lower.
This special issue is to attract articles that cover existing approaches
to dimension reduction based on learning of subspaces or submanifolds --
from linear to nonlinear models, from homogeneous to hybrid models, from
statistical, to geometric, to algebraic, and to graphical methods. We
would also like to feature many successful applications of these new
methods, including but not limited to signal/image processing, pattern
recognition, bioinformatics, and web data mining. Below is an incomplete
list of potential topics to be covered in the special issue:
1. Kernel PCA and Robust PCA with Incomplete and Corrupted Data
2. Generalized PCA and Subspace Arrangements
3. Manifold Learning and Dimension Reduction
4. Learning of Stratification and Submanifold Arrangements
5. Data Clustering and Source Separation Based on Subspace Models
6. Dictionary Learning for Sparse Representation
7. Algebraic, Geometric, and Topological Methods for Manifold Learning and
Clustering
8. Applications of Subspace Analysis, Manifold Learning, and Dimension
Reduction
※Submission Procedure:
Prospective authors should submit their white papers (2 pages maximum) to
the web submission system through IEEE Manuscript Central at:
http://mc.manuscriptcentral.com/spmag-ieee.
※Schedule:
* White paper due: November 1, 2009
* Invitation notification: December 1, 2009
* Manuscript due: April 1, 2010
* Acceptance notification: July 1, 2010
* Final manuscript due: August 1, 2010
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