Topology Preserving Feature Extraction with Multiswarm Optimization

We introduce a new method for feature extraction from object data that is based on the idea of preserving metric topology between the original and derived data sets. Specifically, our method attempts to produce neighbors in the derived data that have the same ranks as in the input data. The algorithm we propose is a novel modification of particle swarm optimization that involves multiswarms. We compare our model and algorithm to feature extraction using Sammon´s method and principal components analysis on 19 data sets: 17 are created by making draws from p-variate Gaussian distributions. We also use two real world data sets - the Glass and Lung Cancer data available at the UCI ML website. We find that the new method compares well with Sammon´s method, and seems to be superior to features derived with principal components analysis.