Localized classification

Localized classification

The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selecti...

Oriented soft localized subspace classification

Subspace methods of pattern recognition form an interesting and popular classiication paradigm. The earliest sub-space method of classiication was the CLass Featuring Information Compression (CLAFIC) which associated with each class a corresponding linear subspace. Local subspace classiication methodologies which have enhanced classii-cation power by associating more than one linear subspace wi...

Multi-scale Classification using Localized Spatial Depth

In this article, we develop and investigate a new classifier based on features extracted using spatial depth. Our construction is based on fitting a generalized additive model to posterior probabilities of different competing classes. To cope with possible multi-modal as well as non-elliptic nature of the population distribution, we also develop a localized version of spatial depth and use that...

EEG Data Classification with Localized Structural Information

The main contribution of this paper is the development of polygon feature selection method for the classification of temporal data from two or more sources on the basis of quantifying structural changes with time. The study focuses on the analysis of EEG data. The paper shows results on the feature classification using a modified fuzzy nearest neighbour method. The transformed inputs are ideall...

Classification Using Localized Mixtures of Experts