Preliminary Results on Noise Detection and Data Selection for Vector Quantization

In this paper we present a data selection methodology for vector quantization. The main goal is to identify, and possibly eliminate, noisy data in a supervised pattern classification context. We consider as ´noise´ an inversion (in a two classes problem) of the class the data belongs to. The methodology is based on two mappings that bring the data selection problem into a R² decision space, independently of the data dimension. The proposed technique demands relatively low computational effort and is model independent. Numerical experiment has shown interesting performance enhancing the potentiality of the method.