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 13, 17–30 (2001). https://doi.org/10.1023/A:1009678928250
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DOI: https://doi.org/10.1023/A:1009678928250