Special session at IEEE CIDM 2014

Call for Papers

Special Session

‘High dimensional data analysis –
theoretical advances and applications’

09-12 December 2014, Orlando, Florida, USA


Modern measurement technology, greatly enhanced storage capabilities
and novel data formats have radically increased the amount and dimensionality
of electronic data. Due to its high dimensionality, complexity and the curse
of dimensionality, these data sets can often not be addressed by classical
statistical methods. Prominent examples can be found in the life sciences
with microarrays, hyper spectral data in geo-sciences but also in fields
like astrophysics, biomedical imaging, finance or web and market basket analysis.

Computational intelligence methods have the potential to be used to pre-process,
model and to analyze such complex data but new strategies are needed to get
efficient and reliable models. Novel data encoding techniques and projection methods, employing concepts
of randomization algorithms have opened new ways to obtain compact
descriptions of these complex data sets or to identify relevant information
but theoretical foundations and the practical potential of these methods and alternative approaches
has still to be explored.

This workshop aims to promote new advances and research directions to address
the modeling, representation/encoding and reduction of high-dimensional data or
approaches and studies adressing challenging problems in the field of high dimensional
data analysis. Topics of interest range from theoretical foundations, to algorithms and implementation,
to applications and empirical studies of mining high dimensional data, including (but not limited to) the following:

o Studies on how the curse of dimensionality affects computational intelligence methods
o New computational intelligence techniques that exploit some properties of high dimensional data spaces
o Theoretical findings addressing the imbalance between high dimensionality and comparable small sample size
o Stability and reliability analyses for data analysis in high dimensions
o Adaptive and non-adaptive dimensionality reduction for noisy high dimensional data sets
o Methods of random projections, compressed sensing, and random matrix theory applied to high dimensional data mining
o Models of low intrinsic dimension, such as sparse representation, manifold models, latent structure models, and studies of their noise tolerance
o Classification, regression, clustering of high dimensional complex data sets
o Functional data mining
o Data presentation and visualisation methods for very high dimensional data sets
o Data mining applications to real problems in science, engineering or businesses where the data is high dimensional

High quality original submissions (upto 8 pages, IEEE style) should follow the guidelines as outlined at the CIDM homepage
and should be submitted using the provided IEEE paper submission system. We strongly encourage to
use the LaTeX stylesheet and not the word format to ensure high quality typesetting and paper representation
also during the review process.

Paper submission deadline : 15 Juni 2014
Notification of acceptance : 05 September 2014
Deadline for final papers : 05 October 2014

The CIDM 2014 conference : 9-12 December 2014

Ata Kaban, University of Birmingham, Birmingham, UK
Frank-Michael Schleif, University of Birmingham, Birmingham, UK
Thomas Villmann, University of Appl. Sc. Mittweida, Germany


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