[Apologies for cross-posting]
The 4-th International Workshop on High Dimensional Data Mining (HDM 2016)
In conjunction with the IEEE International Conference on Data Mining (IEEE ICDM
2016)
http://www.cs.bham.ac.uk/~axk/HDM16.htm
Submission deadline: August 12, 2016.
Notifications to authors: September 13, 2016.
Workshop date: December 12, 2016.
Call For Papers
This workshop aims to promote new advances and research directions to address
the curses, and to uncover and exploit the blessings of high dimensionality in
data mining.
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.
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.
|