1st International Workshop on Similarity-Based Pattern Analysis and Recognition
28-30 September, 2011
Venice, Italy
http://www.dsi.unive.it/~simbad
MOTIVATIONS AND OBJECTIVES
Traditional pattern recognition techniques are intimately linked to
the notion of “feature spaces.” Adopting this view, each object is
described in terms of a vector of numerical attributes and is
therefore mapped to a point in a Euclidean (geometric) vector space so
that the distances between the points reflect the observed
(dis)similarities between the respective objects. This kind of
representation is attractive because geometric spaces offer powerful
analytical as well as computational tools that are simply not
available in other representations. Indeed, classical pattern
recognition methods are tightly related to geometrical concepts and
numerous powerful tools have been developed during the last few
decades, starting from the maximal likelihood method in the 1920’s, to
perceptrons in the 1960’s, to kernel machines in the 1990’s.
However, the geometric approach suffers from a major intrinsic
limitation, which concerns the representational power of vectorial,
feature-based descriptions. In fact, there are numerous application
domains where either it is not possible to find satisfactory features
or they are inefficient for learning purposes. This modeling
difficulty typically occurs in cases when experts cannot define
features in a straightforward way (e.g., protein descriptors vs.
alignments), when data are high dimensional (e.g., images), when
features consist of both numerical and categorical variables (e.g.,
person data, like weight, sex, eye color, etc.), and in the presence
of missing or inhomogeneous data. But, probably, this situation arises
most commonly when objects are described in terms of structural
properties, such as parts and relations between parts, as is the case
in shape recognition.
In the last few years, interest around purely similarity-based
techniques has grown considerably. For example, within the supervised
learning paradigm (where expert-labeled training data is assumed to be
available) the now famous “kernel trick” shifts the focus from the
choice of an appropriate set of features to the choice of a suitable
kernel, which is related to object similarities. However, this shift
of focus is only partial, as the classical interpretation of the
notion of a kernel is that it provides an implicit transformation of
the feature space rather than a purely similarity-based
representation. Similarly, in the unsupervised domain, there has been
an increasing interest around pairwise or even multiway algorithms,
such as spectral and graph-theoretic clustering methods, which avoid
the use of features altogether.
By departing from vector-space representations one is confronted with
the challenging problem of dealing with (dis)similarities that do not
necessarily possess the Euclidean behavior or not even obey the
requirements of a metric. The lack of the Euclidean and/or metric
properties undermines the very foundations of traditional pattern
recognition theories and algorithms, and poses totally new
theoretical/computational questions and challenges.
The workshop will mark the end of the EU FP7 Projects SIMBAD
(http://simbad-fp7.eu), which was devoted precisely to these themes,
and is a follow-up of the ICML 2010 Workshop on “Learning in
non-(geo)metric spaces” (http://www.dsi.unive.it/~icml2010lngs). Its
aim is to consolidate research efforts in this area, and to provide an
informal discussion forum for researchers and practitioners interested
in this important yet diverse subject. The discussion will revolve
around two main themes, which basically correspond to the two
fundamental questions that arise when abandoning the realm of
vectorial, feature-based representations, namely:
– How can one obtain suitable similarity information from data
representations that are more powerful than, or simply different from,
the vectorial?
– How can one use similarity information in order to perform learning
and classification tasks?
We aim at covering a wide range of problems and perspectives, from
supervised to unsupervised learning, from generative to discriminative
models, and from theoretical issues to real-world practical
applications.
Accordingly, topics of interest include (but are not limited to):
– Embedding and embeddability
– Graph spectra and spectral geometry
– Indefinite and structural kernels
– Game-theoretic models of pattern recognition
– Characterization of non-(geo)metric behaviour
– Foundational issues
– Measures of (geo)metric violations
– Learning and combining similarities
– Multiple-instance learning
– Applications
FORMAT
The workshop will feature contributed talks and posters as well as
invited presentations. We feel that the more informal the better, and
we would like to solicit open and lively discussions and exchange of
ideas from researchers with different backgrounds and perspectives.
Plenty of time will be allocated to questions, discussions, and
breaks.
We plan to get videolectures coverage.
ORGANIZATION
Program Chairs
Marcello Pelillo, University of Venice, Italy
Edwin Hancock, University of York, UK
Steering Committee
Joachim Buhmann, ETH Zurich, Switzerland
Robert Duin, Delft University of Technology, The Netherlands
Mario Figueiredo, Technical University of Lisbon, Portugal
Edwin Hancock, University of York, UK
Vittorio Murino, University of Verona, Italy
Marcello Pelillo (chair), University of Venice, Italy
Program Committee
Maria-Florina Balcan, Georgia Institute of Technology, USA
Joachim Buhmann, ETH Zurich, Switzerland
Horst Bunke, University of Bern, Switzerland
Tiberio Caetano, NICTA, Australia
Umberto Castellani, University of Verona, Italy
Luca Cazzanti, University of Washington, Seattle, USA
Nicolò Cesa-Bianchi, University of Milan, Italy
Robert Duin, Delft University of Technology, The Netherlands
Francisco Escolano, University of Alicante, Spain
Mario Figueiredo, Technical University of Lisbon, Portugal
Ana Fred, Technical University of Lisbon, Portugal
Bernard Haasdonk, University of Stuttgart, Germany
Edwin Hancock, University of York, UK
Anil Jain, Michigan State University, USA
Robert Krauthgamer, Weizmann Institute of Science, Israel
Marco Loog, Delft University of Technology, The Netherlands
Vittorio Murino, University of Verona, Italy
Elzbieta Pekalska, University of Manchester, UK
Marcello Pelillo, University of Venice, Italy
Antonio Robles-Kelly, NICTA, Australia
Volker Roth, University of Basel, Switzerland
Andrea Torsello, University of Venice, Italy
Richard Wilson, University of York, UK
Organization Committee
Samuel Rota Bulò (chair), University of Venice, Italy
Nicola Rebagliati, University of Venice, Italy
Luca Rossi, University of Venice, Italy
Teresa Scantamburlo, University of Venice, Italy
IMPORTANT DATES
Paper submission: May 15, 2011
Notifications: June 19, 2011
Camera-ready due: July 2011
Conference: September 28-30, 2011
PAPER SUBMISSION
Papers must be submitted electronically at the conference website
using the EasyChair submission system. Manuscripts should be in pdf
and formatted according to Springer’s Lecture Notes in Computer
Science (LNCS) style. Information concerning typesetting can be
obtained directly from Springer at:
http://www.springer.com/comp/lncs/authors.html.
Papers must not exceed 16 pages and should report original work.
All submitted papers will be subject to a rigorous peer-review
process. Accepted papers will appear in the workshop proceedings,
which will be published in Springer’s Lecture Notes in Computer
Science (LNCS) series.
Submission implies the willingness of at least one of the authors to
register and present the paper, if accepted.
