Efficient vector quantization using the WTA-rule with activity equalization

Heidemann G, Ritter H (2001)
Neural Processing Letters 13(1): 17-30.

Journal Article | Published | English

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Abstract
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.
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Heidemann G, Ritter H. Efficient vector quantization using the WTA-rule with activity equalization. Neural Processing Letters. 2001;13(1):17-30.
Heidemann, G., & Ritter, H. (2001). Efficient vector quantization using the WTA-rule with activity equalization. Neural Processing Letters, 13(1), 17-30.
Heidemann, G., and Ritter, H. (2001). Efficient vector quantization using the WTA-rule with activity equalization. Neural Processing Letters 13, 17-30.
Heidemann, G., & Ritter, H., 2001. Efficient vector quantization using the WTA-rule with activity equalization. Neural Processing Letters, 13(1), p 17-30.
G. Heidemann and H. Ritter, “Efficient vector quantization using the WTA-rule with activity equalization”, Neural Processing Letters, vol. 13, 2001, pp. 17-30.
Heidemann, G., Ritter, H.: Efficient vector quantization using the WTA-rule with activity equalization. Neural Processing Letters. 13, 17-30 (2001).
Heidemann, Gunther, and Ritter, Helge. “Efficient vector quantization using the WTA-rule with activity equalization”. Neural Processing Letters 13.1 (2001): 17-30.
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