Special Session at 2014 IEEE Symposium Series on Computational Intelligence (SSCI)

Organised by the High Dimensional Data Analysis Task Force of the IEEE Computational Intelligence Society

Orlando, Florida / December 9-12, 2014

(from) http://upload.wikimedia.org/wikipedia/commons/2/26/Orlando_Montage.png

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. However theoretical foundations and the practical potential of these methods and alternative approaches has still to be explored and improved. New advances and research to address the curse of dimensions, and to uncover and exploit the blessings of high dimensionality in data analysis are of major interest in theory and application.

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:

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


A. Kaban, F.-M. Schleif and T. Villmann

Important Dates

Paper submission: 15 June 2014, Midnight GMT

Decision: 05 Sept 2014

Final submission: 05 Oct 2014

Early Registration: 05 Oct 2014