Tutorial at IJCNN 2015
Learning in indefinite proximity spaces:
Mathematical foundations, representations and models
Tutorial abstract
Efficient learning of a data analysis task strongly depends on the data representation.
Most methods rely on (symmetric) similarity or dissimilarity representations by means
of metric inner products or distances, providing easy access to powerful mathematical
formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities
are however often naturally obtained by non-metric proximity measures which can not
easily be handled by classical learning algorithms. In the last years major efforts have been undertaken
to provide approaches which can either directly be used for such data or to make standard methods
available for these type of data.
The tutorial provides a comprehensive overview for the field of learning with
non-metric proximities. First we introduce the formalism used in non-metric spaces and motivate
specific treatments for non-metric proximity data. Secondly we provide a systematization of the
various approaches. For each category of approaches a comparative discussion is provided
addressing complexity issues and generalization properties.
We also address the problem of large scale proximity learning which is often overlooked in this
context and of major importance to make the method relevant in practice.
The discussed algorithms and concepts are in general applicable for proximity based clustering, one-class classification,
classification, regression or embedding tasks.
Various applications show the relevance of the discussed approaches, which provide a generic
framework for multiple input formats. The goal of the tutorial is to give an overview about recent
developments in this domain, covering in particular principled approaches as concerns
learning in indefinite spaces and its mathematical foundations and
extensions to large scale problems.
Covered material
The following items represent the topics of the tutorial:
- Indefinite kernels and pseudo-Euclidean spaces: basic definition;
algorithms for correcting indefinite kernels and dissimilarities (eigenspectrum
and proxy approaches)
- Models for supervised learning in pseudo-Euclidean spaces and by general
similarity functions
- Application examples from the biomedical domain and image processing
- Approximation strategies improving runtime, memory complexity and out-of-sample extension
URLs
Tutorial material is available as pdf-slides. The mentioned survey paper
(accepted by Neural Computation and being open access soon) can be obtained as a preprint.
Sources
All packages listed below are implemented in Matlab but may also contain c/c++ bindings.
(If any link is dead please let me know I will likely be able to help in providing an alternative source)
Organizers
Peter Tino,
School of Computer Science, University of Birmingham, UK,
Email: P.Tino@cs.bham.ac.uk
http://www.cs.bham.ac.uk/~pxt
Frank-Michael Schleif,
School of Computer Science, University of Birmingham, UK,
Email: schleify@cs.bham.ac.uk
http://www.cs.bham.ac.uk/~schleify
Peter Tino
is Professor of Complex and Adaptive Systems at the School of
Computer Science, University of Birmingham, UK. He held a Fulbright
Fellowship (at NEC Research Institute, Princeton, USA) and a UK-Hong
Kong Fellowship for Excellence. Peter is a recipient of three IEEE
Computational Intelligence Society Outstanding Paper of the Year awards
in IEEE Transactions on Neural Networks (1998, 2011) and IEEE
Transactions on Evolutionary Computation (2010). He serves on Editorial
Boards of several journals (IEEE Transactions on Neural Networks and
Learning Systems (IEEE CIS, since 2013), Scientific Reports (Nature
Publishing, since 2011) and Neural Processing Letters (Springer, since
2007)). He has (co-)chaired programme committee of 4 international
conferences, while being
member on programme committees of more than 90 international conferences.
Peter's scientific interests include machine learning, dynamical
systems, evolutionary computation, complex systems, probabilistic
modelling and statistical pattern recognition.
Frank-Michael Schleif
received his Ph.D. in Computer Science from the University of Clausthal, Germany, in 2006
and his venia legendi in Applied Computer Science in 2013 from the University of Bielefeld, Germany.
From 2004-2006 he was working for the R\&D department at Bruker Biosciences. From 2006 to 2009 he was a research assistant
in the research group of computational intelligence at the University of Leipzig
working on multiple bioinformatic projects. In 2010 he joined the Chair of Theoretical Computer Science at the
University of Bielefeld and did research in multiple projects in machine learning and bioinformatics.
Since 2014 he is a member of the University of Birmingham, UK as a Marie Curie Fellow and PI of the project
Probabilistic Models in Pseudo-Euclidean Spaces. His areas of expertise include machine learning, signal
processing, data analysis and bioinformatics. Several long term research stays
have taken him to UK, the USA, the Netherlands and Japan. He is co-editor of the
Machine Learning Reports and reviewer for multiple
journals and conferences in the field of machine learning
and computational intelligence. He is a
founding member of the Institute of Computational Intelligence
and Intelligent Data Analysis (CIID) e.V. (Mittweida, Germany), a
member of the IEEE-CIS, IEEE-SPS, the GI, the DAGM and secretary of the German chapter of the ENNS (GNNS).
He is coauthor of more than 100 papers in international journals
and conferences on different aspects of Computational Intelligence, most of
which can be retrieved from http://www.cs.bham.ac.uk/~schleify
