Connectionists: Early call - special session - 'Indefinite proximity learning' (ESANN'16)

[Frank-Michael Schleif]

Call for Papers Special Session on

  'Indefinite proximity learning'
      27-29 April 2016, Bruges, Belgium
http://www.dice.ucl.ac.be/esann

AIMS AND SCOPE

Today real life data are often not given as vectorial data,
but by means of proximities (similarities or dissimilarities) only,
calculated by an appropriate proximity function. The pairwise
proximity matrix may be a symmetric positive semidefinite matrix - and
hence a kernel
matrix but can be much more generic.

Often the underlying data may not exist in a vector space and the
proximity function
violates metric properties, leading to indefinite, potentially
asymmetric proximity matrices, which can not directly be processed by
classical machine learning algorithms - like kernel machines.

We can find these settings in various domains like
the analysis of text documents - using e.g. the compression distance,
the comparison of biological sequence data - with domain specific
alignment measures, shape retrieval problems in robotics using the
inner distance, the representation of structured data like graphs or
trees and many other applications.

The recent technological developments, also in the context of big data,
allow the generation of very large data sets. If the data are represented
by non-metric proximities the processing becomes particular challenging,
because many classical mathematical models require metric properties.

Dedicated processing strategies for non-metric proximity data
(indefinite proximities, non-positive kernels, dissimilarity data)
are of wide interest and the subject of this special session.

TOPICS
We encourage submission of papers on novel methods for (pre-) processing
of non-metric kernels, structured data or in the field of non-metric
dissimilarity learning by means of computational intelligence and
machine learning approaches, including but not limited to:

    - data analysis and pattern recognition approaches for (indefinite)
      proximity data, structured data and dissimilarity learning
    - clustering, classification, regression, embedding approaches for
indefinite data
    - approaches in the line of matrix completion, collaborative filtering,
      reduction techniques for non-standard data
    - low rank matrix approaches - valid for indefinite proximities
    - vector space embedding
    - metric nearness and correction approaches
    - large scale proximity matrix analysis and handling
    - quality and error measures for indefinite data representations
    - applications with indefinite proximity data

IMPORTANT DATES
Paper submission deadline : 20 November 2015
Notification of acceptance : 31 January 2016
The ESANN 2014 conference : 27-29 April 2016

SPECIAL SESSION ORGANIZERS:
Frank-Michael Schleif, University of Birmingham, Birmingham, UK and
University of Appl. Sc. Mittweida, Germany
Peter Tino, University of Birmingham, Birmingham, UK
Yingyu Liang, Princeton University, USA

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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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