Separable Linearization of Learning Sets by Ranked Layer of Radial Binary Classifiers

摘要

Layers of binary classifiers can be used in transformation of data sets composed of multivariate feature vectors. A new representation of data sets is obtained this way that depends on parameters of the classifiers in the layer. By a special, data driven choice of these parameters the ranked layer can be designed. The ranked layer has a important property of data sets linearization. It means that the data sets become linearly separable after transformation by ranked layer. The ranked layer can be built, inter alia, from radial or nearest neighbors binary classifiers.