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LBGS:: a smart approach for very large data sets vector quantization
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DOI：10.1016/j.image.2004.10.001.png)
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Abstract
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In this paper, LBGS, a new parallel/distributed technique for Vector Quantization is presented. It derives from the well known LBG algorithm and has been designed for very complex problems where both large data sets and large codebooks are involved. Several heuristics have been introduced to make it suitable for implementation on parallel/distributed hardware. These lead to a slight deterioration of the quantization error with respect to the serial version but a large improvement in computing efficiency. (C) 2004 Elsevier B.V. All rights reserved.
Keywords:
clustering
vector quantization
unsupervised learning
parallel
distributed
learning
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