Automated feature selection with a distinction sensitive learning vector quantizer

Abstract

An extended version of Kohonen’s Learning Vector Quantization (LVQ) algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection. The algorithm employs a weighted distance function and adapts the metric with learning. Highest weights are assigned to components in the input vectors which are most informative for classification; non-informative components are discarded. The algorithm is applied to the analyses of multi-channel EEG data and compared with experienced methods.

DOI: 10.1016/0925-2312(94)00071-9

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@article{Pregenzer1996AutomatedFS, title={Automated feature selection with a distinction sensitive learning vector quantizer}, author={Martin Pregenzer and Gert Pfurtscheller and Doris Flotzinger}, journal={Neurocomputing}, year={1996}, volume={11}, pages={19-29} }