Apologies if multiple copies are received.
Call for Papers:
NIPS 2008 WORKSHOP on LEARNING FROM MULTIPLE SOURCES
While the machine learning community has primarily focused on
analysing output of a single data source, there has been relatively
few attempts to develop a general framework, or heuristics, for
analysing several data sources in terms of a shared dependency
structure. Learning from multiple data sources (or alternatively, the
data fusion problem) is a timely research area. Due to the increasing
availability and sophistication of data recording techniques and
advances in data analysis algorithms, there exists many scenarios in
which it is necessary to model multiple, related data sources, i.e. in
fields such as bioinformatics, multimodal signal processing,
information retrieval etc. The relevance of this research area is
inspired by the human brain's ability to integrate five different
sensory input streams into a coherent representation of its environment.
The open question is to find approaches to analyse data which consists
of more than one set of observations (or view) of the same phenomenon.
In general, existing methods use a discriminative approach, where a
set of features for each data set is found in order to explicitly
optimise some dependency criterion. Existing approaches include
canonical correlation analysis (Hotelling, 1936), a standard
statistical technique for modeling two data sources, and its multiset
variation (Kettenring, 1971) which find linearly correlated features
between data sets, and kernel variants (Lai and Fyfe, 2000; Bach and
Jordan, 2002; Hardoon et al., 2004) and approaches that optimise the
mutual information between extracted features (Becker, 1996; Chechik
et al., 2003). However, discriminative approaches may be ad hoc,
require regularisation to ensure erroneous shared features are not
discovered, and it is difficult to incorporate prior knowledge about
the shared information. Generative probabilistic approaches address
this problem by jointly modeling each data stream as a sum of a shared
component and a 'private' component that models the within-set
variation (Bach and Jordan, 2005; Leen and Fyfe, 2006; Klami and
These approaches assume a simple relationship between (two) data
sources, i.e.assuming a so-called 'flat' data structure where the data
consists of N independent pairs of related data variables; whereas in
practice, related data sources may exhibit extremely complex co-
variation (for instance, audio and visual streams related to the same
video). A potential solution to this problem could be a fully
probabilistic approach, which could be used to impose structured
variation within and between data sources. Additional methodological
challenges include determining what is the 'useful' information we are
trying to learn from the multiple sources and building models for
predicting one data source given the others. As well as the
unsupervised learning of multiple data sources detailed above, there
is the closely related problem of multitask learning (Bickel et al.,
2008), or transfer learning, where a task is learned from other
The aim of the workshop is to promote discussion amongst leading
machine learning and applied researchers about learning from multiple,
related sources of data, with a focus on both methodological issues
and applied research problems.
Topics of the workshop include (but not limited to):
- unsupervised learning (generative / discriminative modeling) of
multiple related data sources
- canonical correlation analysis-type methods
- data fusion for real world applications, such as bioinformatics,
sensor networks, multimodal signal processing, information retrieval
- multitask /transfer learning
- multiview learning
Prof. Michael Jordan
University of California, Berkeley
Dr. Francis Bach
École normale supérieure
Dr. Tobias Scheffer
Max-Planck-Institut fur Informatik
David R. Hardoon (University College London)
Gayle Leen (Helsinki University of Technology)
Samuel Kaski (Helsinki University of Technology)
John Shawe-Taylor (University College London)
Andreas Argyriou (University College London)
Tom Dieithe (University College London)
Colin Fyfe (University of the West of Scotland)
Jaakko Peltonen (Helsinki University of Technology)
We invite the submission of high quality extended abstracts (2 to 4
pages) in the NIPS style http://nips.cc/PaperInformation/StyleFiles.
Abstracts should be sent (in .pdf/.ps) to [EMAIL PROTECTED], [EMAIL PROTECTED]
A selection of the submitted abstracts will be accepted as either an
oral presentation or poster presentation. The best abstracts will be
considered for extended versions in the workshop proceedings, and
possibly as a special issue of a journal.
24 Oct 08 Submission deadline for extended abstracts
28 Oct 08 Notification of acceptance
13 Dec 08 Workshop at NIPS 08, Whistler, Canada
BACH, F.R., & JORDAN, M.I. 2002. Kernel Independent Component
Analysis. Journal of Machine Learning, 3, 1-48.
BACH, F.R., & JORDAN, M.I. 2005. A Probabilistic Interpretation of
Canonical Correlation Analysis. Tech. rept. 688. Dept of Statistics,
University of California.
BECKER, S. 1996. Mutual Information Maximization: models of cortical
selforganisation. Network: Computation in Neural Systems, 7, 7-31.
BICKEL, S., BOGOJESKA, J., LENGAUER, T., & SCHEFFER, T. Multi-task
learning for HIV therapy screening. ICML 2008
CHECHIK, G., GLOBERSON, A., TISHBY, N., & WEISS, Y. 2003. Information
Bottleneck for Gaussian variables. Pages 1213-1220 of: THRUN, S.,
SAUL, L.K., & SCH¨OLKOPF, B. (eds), Advances in Neural Information
Processing Systems, vol. 16.
HARDOON, D. R., SZEDMAK, S. & SHAWE-TAYLOR, J. 2004 Canonical
Correlation Analysis: An Overview with Application to Learning
Methods. Neural Computation, 16(12), 2639-2664
HOTELLING, H. 1936. Relations between two sets of variates.
Biometrika, 28, 312-377.
KETTENRING, J. R. 1971. Canonical analysis of several sets of
variables. Biometrika, 58(3), 433-451.
KLAMI, A., & KASKI, S. 2006. Generative models that discover
dependencies between two data sets. Pages 123-128 of: MCLOONE, S.,
ADALI, T., LARSEN, J., HULLE, M. VAN, ROGERS, A., & DOUGLAS, S.C.
(eds), Machine Learning for
Signal Processing XVI. IEEE.
LAI, P. L., & FYFE, C. 2000. Kernel and Nonlinear Canonical
Correlation Analysis. International Journal of Neural Systems, 10(5),
LEEN, G., & FYFE, C. 2006. A Gaussian Process Latent Variable Model
Formulation of Canonical Correlation Analysis. Pages 413-418 of:
Proceedings of the 14th European Symposium of Artificial Neural
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