index.rst

Learning Vector Quantization

Learning Vector quantization (LVQ) attempts to construct a highly sparse model of the data by representing data classes by prototypes. Prototypes are vectors in the data spaced which are placed such that they achieve a good nearest-neighbor classification accuracy. More formally, for a dataset {(x₁, y₁), ..., (x_m, y_m)} LVQ attempts to place K prototypes w₁, ..., w_K with labelsc₁, ..., c_K in the data space, such that as many data points as possible are correctly classified by assigning the label of the closest prototype. The number of prototypes K is a hyper-parameter to be specified by the user. Per default, we use 1 prototype per class.

Contents:

modules/api auto_examples/index glvq rslvq

Dimensionality Reducation

sklearn_lvq

The relevances learned by a GrlvqModel,GmlvqModel,LgmlvqModel,MrslvqModel and LmrslvqModel can be applied for dimensionality reduction by projecting the data on the eigenvectors of the relevance matrix which correspond to the largest eigenvalues.

References: