---
_id: '1617662'
abstract:
- lang: eng
text: We propose a new algorithm for vector quantization, the Activity Equalization
Vector quantization (AEV). It is based on the winner takes all rule with an additional
supervision of the average node activities over a training interval and a subsequent
re-positioning of those nodes with low average activities. The re-positioning
is aimed to both an exploration of the data space and a better approximation of
already discovered data clusters by an equalization of the node activities. We
introduce a learning scheme for AEV which requires as previous knowledge about
the data only their bounding box. Using an example of Martinetz et al. [1], AEV
is compared with the Neural Gas, Frequency Sensitive Competitive Learning (FSCL)
and other standard algorithms. It turns out to converge much faster and requires
less computational effort.
accept: '1'
article_type: original
author:
- autoren_ansetzung:
- Heidemann, Gunther
- Heidemann
- Gunther Heidemann
- Heidemann, G
- Heidemann, G.
- G Heidemann
- G. Heidemann
first_name: Gunther
full_name: Heidemann, Gunther
last_name: Heidemann
- autoren_ansetzung:
- Ritter, Helge
- Ritter
- Helge Ritter
- Ritter, H
- Ritter, H.
- H Ritter
- H. Ritter
first_name: Helge
full_name: Ritter, Helge
id: '91130'
last_name: Ritter
citation:
ama: Heidemann G, Ritter H. Efficient vector quantization using the WTA-rule with
activity equalization. *Neural Processing Letters*. 2001;13(1):17-30.
angewandte-chemie: G. Heidemann, and H. Ritter, “Efficient vector quantization
using the WTA-rule with activity equalization”, *Neural Processing Letters*,
**2001**, *13*, 17-30.
apa: Heidemann, G., & Ritter, H. (2001). Efficient vector quantization using
the WTA-rule with activity equalization. *Neural Processing Letters*, *13*(1),
17-30. doi:10.1023/A:1009678928250
apa_indent: Heidemann,
G., & Ritter, H. (2001). Efficient vector quantization using the WTA-rule
with activity equalization. *Neural Processing Letters*, *13*(1),
17-30. doi:10.1023/A:1009678928250

aps: ' G. Heidemann and H. Ritter, Efficient vector quantization using the WTA-rule
with activity equalization, Neural Processing Letters **13**, 17
(2001).'
bio1: 'Heidemann G, Ritter H (2001)

Efficient vector quantization using the
WTA-rule with activity equalization.

Neural Processing Letters 13(1): 17-30.'
chicago: 'Heidemann,
Gunther, and Ritter, Helge. 2001. “Efficient vector quantization using the WTA-rule
with activity equalization”. *Neural Processing Letters* 13 (1): 17-30.

'
default: 'Heidemann G, Ritter H (2001)

*Neural Processing Letters*
13(1): 17-30.'
dgps: Heidemann,
G. & Ritter, H. (2001). Efficient vector quantization using the WTA-rule with
activity equalization. *Neural Processing Letters*, *13*(1), 17-30.
KLUWER ACADEMIC PUBL. doi:10.1023/A:1009678928250.

frontiers: Heidemann, G., and Ritter, H. (2001). Efficient vector quantization using
the WTA-rule with activity equalization. *Neural Processing Letters* 13,
17-30.
harvard1: Heidemann, G., & Ritter, H., 2001. Efficient vector quantization using
the WTA-rule with activity equalization. *Neural Processing Letters*, 13(1),
p 17-30.
ieee: ' G. Heidemann and H. Ritter, “Efficient vector quantization using the WTA-rule
with activity equalization”, *Neural Processing Letters*, vol. 13, 2001, pp.
17-30.'
lncs: ' Heidemann, G., Ritter, H.: Efficient vector quantization using the WTA-rule
with activity equalization. Neural Processing Letters. 13, 17-30 (2001).'
mla: 'Heidemann, Gunther, and Ritter, Helge. “Efficient vector quantization using
the WTA-rule with activity equalization”. *Neural Processing Letters* 13.1
(2001): 17-30.'
wels: 'Heidemann, G.; Ritter, H. (2001): Efficient vector quantization using the
WTA-rule with activity equalization *Neural Processing Letters*,13:(1):
17-30.'
date_created: 2010-04-28T13:03:21Z
date_submitted: 2011-06-15T19:45:42Z
date_updated: 2018-07-24T12:59:31Z
department:
- _id: '10038'
- _id: '10066'
doi: 10.1023/A:1009678928250
external_id:
isi:
- '000167731600002'
first_author: Heidemann, Gunther
id: '1617662'
intvolume: ' 13'
isi: '1'
issue: '1'
keyword:
- clustering
- neural gas
- codebook generation
- vector quantization
- competitive learning
- unsupervised learning
- winner takes all
language:
- iso: eng
page: 17-30
publication: Neural Processing Letters
publication_identifier:
issn:
- 1370-4621
publication_status: published
publisher: KLUWER ACADEMIC PUBL
quality_controlled: '1'
status: public
title: Efficient vector quantization using the WTA-rule with activity equalization
type: journal_article
volume: '13'
year: '2001'
...