Package smile.vq

package smile.vq

Vector quantization is a lossy compression technique used in speech and image coding. In vector quantization, a vector is selected from a finite list of possible vectors to represent an input vector of samples. Each input vector can be viewed as a point in an n-dimensional space. The vector quantizer is defined by a partition of this space into a set of non-overlapping regions. The vector is encoded by the nearest reference vector (known as codevector) in the codebook.

Related Packages

Package	Description
smile.vq.hebb	Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell.

Class	Description
BIRCH	Balanced Iterative Reducing and Clustering using Hierarchies.
GrowingNeuralGas	Growing Neural Gas.
Neighborhood	The neighborhood function for 2-dimensional lattice topology (e.g.
NeuralGas	Neural Gas soft competitive learning algorithm.
NeuralMap	NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.
SOM	Self-Organizing Map.
VectorQuantizer	Vector quantizer with competitive learning.