Learning Vector Quantization¶
Learning Vector quantization (LVQ) 1 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 LVQ attempts to place K prototypes with 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:
Dimensionality Reducation¶
The relevances learned by a GrlvqModel
,:class:GmlvqModel,:class:LgmlvqModel,:class: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:
- 1
“Learning Vector Quantization” Kohonen, Teuvo - Self-Organizing Maps, pp. 175-189, 1995.