Connectionists: Review paper about Indefinite Proximity Learning

[Frank-Michael Schleif]

Dear all,

I would like to point you to our recently published
review paper about ''Indefinite Proximity Learning''
published at NECO which is now also available
as --- open access --- article

http://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00770#.ViD6Et-oHCI

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 nonmetric
proximity measures that cannot easily be handled by classical learning
algorithms. Major efforts have been undertaken to provide approaches
that can either directly be used for such data or to make standard
methods available for these types of data. We provide a comprehensive
survey for the field of learning with nonmetric proximities. First, we
introduce the formalism used in nonmetric spaces and motivate specific
treatments for nonmetric proximity data. Second, we provide a
systematization of the various approaches. For each category of
approaches, we provide a comparative discussion of the individual
algorithms and address complexity issues and generalization
properties. In a summarizing section, we provide a larger experimental
study for the majority of the algorithms on standard data sets. 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 algorithms we discuss are in general
applicable for proximity-based clustering, one-class classification,
classification, regression, and embedding approaches. In the
experimental part, we focus on classification tasks.


all the best

Frank-Michael Schleif

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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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