Package smile.vq

Originally used for data compression, Vector quantization (VQ) allows the modeling of probability density functions by the distribution of prototype vectors.

See: Description

Class Summary

Class	Description
GrowingNeuralGas	Growing Neural Gas.
GrowingNeuralGas.Neuron	The neuron vertex in the growing neural gas network.
NeuralGas	Neural Gas soft competitive learning algorithm.
NeuralMap	NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.
NeuralMap.Neuron	The neurons in the network.
SOM	Self-Organizing Map.
SOM.Neuron	Self-Organizing Map Neuron.

Package smile.vq Description

Originally used for data compression, Vector quantization (VQ) allows the modeling of probability density functions by the distribution of prototype vectors. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in K-Means and some other clustering algorithms.

Vector quantization is is based on the competitive learning paradigm, and also closely related to sparse coding models used in deep learning algorithms such as autoencoder.

Algorithms in this package also support the partition method for clustering purpose.