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Information Theoretic Vector Quantization with Fixed Point Updates
Rao, S.; Seungju Han; Principe, J.;
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
12-17 Aug. 2007
Page(s):1020
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1024
Abstract:
In this paper, we revisit information theoretic vector quantization (ITVQ) algorithm introduced in (T. Lehn-Schioler et al., 2005) and make it practical. We derive a fixed point update rule to minimize the Cauchy-Schwartz(CS) pdf divergence between the set of codewords and the actual data. In doing so, we overcome two severe deficiencies of the previous gradient based method namely, the number of parameters to be optimized and slow convergence rate, thus making this algorithm more efficient and useful as a compression algorithm.
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