Vector quantization - Open Access .click

Neural Computation, 2003
Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function
Seo, Sambu, Obermayer, Klaus
openaire +3 more sources

Divergence-Based Vector Quantization

Neural Computation, 2011
Supervised and unsupervised vector quantization methods for classification and clustering traditionally use dissimilarities, frequently taken as Euclidean distances. In this article, we investigate the applicability of divergences instead, focusing on online learning.
Villmann, Thomas, Haase, Sven
openaire +3 more sources

Two-stage vector quantization-lattice vector quantization

IEEE Transactions on Information Theory, 1995
Summary: A two-stage vector quantizer is introduced that uses an unstructured first-stage codebook and a second-stage lattice codebook. Joint optimum two-stage encoding is accomplished by exhaustive search of the parent codebook of the two-stage product code.
Pan, Jianping, Fischer, Thomas R.
openaire +1 more source

computer science
artificial intelligence
algorithm

mathematics
quantization signal processing
pattern recognition psychology

physics
learning vector quantization
codebook