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Vector Quantization (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.