Information Theoretic Vector Quantization with Fixed Point Updates
Sudhir Rao, Seungju Han, José Carlos Príncipe · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
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.