The 5-th International Workshop on High Dimensional Data Mining (HDM
2017) @ IEEE International Conference on Data Mining (IEEE ICDM 2017)
http://www.cs.bham.ac.uk/~axk/HDM17.htm
*** Extended Submission Deadline: August 14, 2017 ***
Notifications to authors: September 7, 2017.
Workshop date: November 18th 2017.
Call For Papers
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. Geometric intuition breaks down, statistical
estimation becomes problematic. Classical data analysis methods become
inadequate, questionable, or inefficient at best, and this calls for new
approaches.
This workshop aims to bring together researchers from data mining,
machine learning, statistics, and related disciplines, to promote new
advances and research directions that address the curse of
dimensionality. Topics of interest include theoretical foundations,
algorithms and implementation, as well as applications and empirical
studies, for example:
o Systematic studies of how the curse of dimensionality affects data
mining methods
o Models of low intrinsic dimension: sparse representation, manifold
models, latent structure models, large margin, others
o How to exploit intrinsic dimension in optimisation tasks for data
mining
o New data mining techniques that scale with the intrinsic dimension, or
that exploit some properties of high dimensional data spaces
o Dimensionality reduction
o Methods of random projections, compressed sensing, and random matrix
theory applied to high dimensional data mining and high dimensional
optimisation
o Theoretical underpinning of mining data whose dimensionality is larger
than the sample size
o Classification, regression, clustering, visualisation 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 are solicited. Papers should not
exceed 8 pages, and should follow the IEEE ICDM format requirements of
the main conference. All submissions will be peer-reviewed, and the
accepted papers will be published in the proceedings by the IEEE
Computer Society Press.
You may leave the list at any time by sending the command
SIGNOFF allstat
to [log in to unmask], leaving the subject line blank.
|