Abstract
A new vector quantization method (LBG-U) closely related to a particular class of neural network models (growing self-organizing networks) is presented. LBG-U consists mainly of repeated runs of the well-known LBG algorithm. Each time LBG converges, however, a novel measure of utility is assigned to each codebook vector. Thereafter, the vector with minimum utility is moved to a new location, LBG is run on the resulting modified codebook until convergence, another vector is moved, and so on. Since a strictly monotonous improvement of the LBG-generated codebooks is enforced, it can be proved that LBG-U terminates in a finite number of steps. Experiments with artificial data demonstrate significant improvements in terms of RMSE over LBG combined with only modestly higher computational costs.
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Fritzke, B. The LBG-U Method for Vector Quantization – an Improvement over LBG Inspired from Neural Networks. Neural Processing Letters 5, 35–45 (1997). https://doi.org/10.1023/A:1009653226428
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DOI: https://doi.org/10.1023/A:1009653226428