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(VQ) is highly competitive technique for automatic speaker identification compared with Gaussian Mixture Model (GMM) (Kinnunen et al.
A strategy to cluster very large data sets is to perform vector quantization
followed by a clustering of the obtained prototypes.
In this paper, we consider distributed vector quantization
. When using a sensor network as an exploratory infrastructure, we need to transmit and store the data collected by sensors.
Genetic Algorithm and Vector Quantization
. The vector quantization
(VQ) approach is used for mapping vectors (feature extraction of input signal) from a large vector space to a finite number of regions in that space.
In this model, with vector quantization
(VQ), descriptor size can be reduced significantly; for example, in the test model (Francini et al.
Applications to Pulse Code Modulation and Vector Quantization
of Images are addressed.
Linear Vector Quantization
using Bit Mapping: In Bit-mapping LVQ the segmented characters are directly fed as an input to Neural Network for training.