Advanced LVQ with Use of Neighborhood Information on Data Observation Points and Its Application to Intravascular Ultrasound Data

Abstract

We propose a novel classification method using a learning vector quantization, which utilizes not only the feature vectors but also the neighborhood information on data observation points. The proposed method achieves an effective classification even if the boundary of each class is overlapped in the feature space. The effectiveness of the proposed method is verified by applying it to the tissue classification problem of the intravascular ultrasound radiofrequency data.