Connectionists: Extended (final) deadline - ICDM Workshop on High Dimensional Data Mining (HDM'14)

[Frank-Michael Schleif]

  +++ PLEASE, APOLOGIZE MULTIPLE COPIES +++
    +++ Extended (final) deadline: August 17, 2014 +++
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                                Call for Papers
        The 2nd International Workshop on High Dimensional Data Mining (HDM'14)
                        http://www.cs.bham.ac.uk/~axk/HDM14.htm
                           http://hdataskforce.wordpress.com/

                       In conjunction with the
        IEEE International Conference on Data Mining (IEEE ICDM 2014)
                      http://icdm2014.sfu.ca/home.html

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Description of Workshop

Stanford statistician David Donoho predicted that the 21st century
will be the century of data. "We can say with complete confidence that
in the coming century, high-dimensional data analysis will be a very
significant activity, and completely new methods of high-dimensional
data analysis will be developed; we just don't know what they are
yet." -- D. Donoho, 2000.

Beyond any doubt, unprecedented technological advances lead to
increasingly high dimensional data sets in all areas of science,
engineering and businesses. These include genomics and proteomics,
biomedical imaging, signal processing, astrophysics, finance, web and
market basket analysis, among many others. The number of features in
such data is often of the order of thousands or millions - that is
much larger than the available sample size.

A number of issues make classical data analysis methods inadequate,
questionable, or inefficient at best when faced with high dimensional
data spaces:
 1. High dimensional geometry defeats our intuition rooted in low
dimensional experiences, and this makes data presentation and
visualisation particularly challenging.
 2. Phenomena that occur in high dimensional probability spaces, such
as the concentration of measure, are counter-intuitive for the data
mining practitioner. For instance, distance concentration is the
phenomenon that the contrast between pair-wise distances may vanish as
the dimensionality increases. This makes the notion of nearest
neighbour meaningless, together with a number of methods that rely on
a notion of distance.
 3. Bogus correlations and misleading estimates may result when trying
to fit complex models for which the effective dimensionality is too
large compared to the number of data points available.
 4. The accumulation of noise may confound our ability to find low
dimensional intrinsic structure hidden in the high dimensional data.
 5. The computation cost of processing high dimensional data or
carrying out optimisation over a high dimensional parameter spaces is
often prohibiting.

Topics

This workshop aims to promote new advances and research directions to
address the curses and uncover and exploit the blessings of high
dimensionality in data mining. Topics of interest include (but are not
limited to):

- Systematic studies of how the curse of dimensionality affects data
mining methods
- New data mining techniques that exploit some properties of high
dimensional data spaces
- Theoretical underpinning of mining data whose dimensionality is
larger than the sample size
- Stability and reliability analyses for data mining in high dimensions
- Adaptive and non-adaptive dimensionality reduction for noisy high
dimensional data sets
- Methods of random projections, compressed sensing, and random matrix
theory applied to high dimensional data mining and high dimensional
optimisation
- Models of low intrinsic dimension, such as sparse representation,
manifold models, latent structure models, and studies of their noise
tolerance
- Classification of high dimensional complex data sets
- Functional data mining
- Data presentation and visualisation methods for very high
dimensional data sets
- Data mining applications to real problems in science, engineering or
businesses where the data is high dimensional

Paper submission
High quality original submissions are solicited for oral and poster
presentation at the workshop. Papers should not exceed a maximum of 8
pages, and must follow the IEEE ICDM format requirements of the main
conference. All submissions will be peer-reviewed, and all accepted
workshop papers will be published in the proceedings by the IEEE
Computer Society Press. Submit your paper here.

Important dates
Extended deadline: August 17, 2014 (no further extension)
Notifications to authors: September 26, 2014
Workshop date: December 14, 2014

We are looking forward to welcome you in Shenzhen
with best regards

Ata Kaban
Frank-Michael Schleif
Thomas Villmann
(Workshop Organizers)

--
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PD Dr. rer. nat. habil. Frank-Michael Schleif
School of Computer Science
The University of Birmingham
Edgbaston
Birmingham B15 2TT
United Kingdom
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email: fschleif at techfak.uni-bielefeld.de
http://promos-science.blogspot.de/
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