TNNLS 2014 - Special Issue on Learning in Non-(geo)metric Spaces

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Special Issue on
Learning in Non-(geo)metric Spaces
Traditional machine learning and pattern recognition techniques are
intimately linked to the notion of “feature space.” 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. 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. 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 machine learning theories and algorithms, and poses
totally new theoretical/computational questions and challenges that
the research community is currently trying to address. The goal of the
special issue is to consolidate research efforts in this area by
soliciting and publishing high-quality papers which, together, will
present a clear picture of the state of the art.
SCOPE OF THE SPECIAL ISSUE
We will encourage submissions of papers addressing theoretical,
algorithmic, and practical issues related 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?
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 and learning
- Characterization of non-(geo)metric behavior
- Foundational issues
- Measures of (geo)metric violations
- Learning and combining similarities
- Multiple-instance learning
- Applications
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 applications.
IMPORTANT DATES
October 1, 2013 ? Deadline for manuscript submission
April 1, 2014 ? Notification to authors
July 1, 2014 ? Deadline for submission of revised manuscripts
October 1, 2014 ? Final decision
GUEST EDITORS
Marcello Pelillo, Ca Foscari University,Venice, Italy (pelillo-AT-dsi.unive.it)
Edwin Hancock, University of York, UK (edwin.hancock-AT-york.ac.uk)
Xuelong Li, Chinese Academy of Sciences, China (xuelong_li-AT-ieee.org)
Vittorio Murino, Italian Institute of Technology, Italy
(vittorio.murino-AT-univr.it)
SUBMISSION INSTRUCTIONS
1. Read the information for authors at: http://cis.ieee.org/publications.html
2. Submit the manuscript by October 1, 2013 at the IEEE-TNNLS webpage
(http://mc.manuscriptcentral.com/tnnls) and follow the submission
procedure. Please, clearly indicate on the first page of the
manuscript and in the author's cover letter that the manuscript has
been submitted to the Special Issue on Learning in non-(geo)metric
spaces. Send also an e-mail to the guest editors to notify them of
your submission.
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Prof. Marcello Pelillo, FIEEE, FIAPR
Professor of Computer Science
Computer Vision and Pattern Recognition Lab, Director
Center for Knowledge, Interaction and Intelligent Systems (KIIS), Director
DAIS
Ca' Foscari University, Venice
Via Torino 155, 30172 Venezia Mestre, Italy
Tel: (39) 041 2348.440
Fax: (39) 041 2348.419
E-mail: marcello.pelillo-AT-gmail.com
URL: http://www.dsi.unive.it/~pelillo