γ-Observable neighbours for vector quantization
We define the γ-observable neighbourhood and use it in soft-competitive learning for vector quantization. Considering a datum v and a set of n units w i in a Euclidean space, let v i be a point of the segment [ vw i ] whose position depends on γ a real number between 0 and 1, the γ-observable neighb...
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Veröffentlicht in: | Neural networks 2002-10, Vol.15 (8), p.1017-1027 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | We define the
γ-observable neighbourhood and use it in soft-competitive learning for vector quantization. Considering a datum
v and a set of
n units
w
i
in a Euclidean space, let
v
i
be a point of the segment [
vw
i
] whose position depends on
γ a real number between 0 and 1, the
γ-observable neighbours (
γ-ON) of
v are the units
w
i
for which
v
i
is in the Voronoı̈ of
w
i
, i.e.
w
i
is the closest unit to
v
i
. For
γ=1,
v
i
merges with
w
i
, all the units are
γ-ON of
v, while for
γ=0,
v
i
merges with
v, only the closest unit to
v is its
γ-ON. The size of the neighbourhood decreases from
n to 1 while
γ goes from 1 to 0. For
γ lower or equal to 0.5, the
γ-ON of
v are also its natural neighbours, i.e. their Voronoı̈ regions share a common boundary with that of
v. We show that this neighbourhood used in Vector Quantization gives faster convergence in terms of number of epochs and similar distortion than the Neural-Gas on several benchmark databases, and we propose the fact that it does not have the dimension selection property could explain these results. We show it also presents a new self-organization property we call ‘self-distribution’. |
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ISSN: | 0893-6080 1879-2782 |
DOI: | 10.1016/S0893-6080(02)00076-X |