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