Novel L1-Based Neural Gas Clustering Algorithms

Clustering algorithms of the Neural Gas (NG) type take into consideration the dissimilarities between prototypes in the original input space. It has been successfully applied in vector quantization, topology creation as well as clustering. NG algorithms conventionally are based on the squared Euclidean distance or L2 distance, which has several known setbacks (not robust to noise and outliers). Our goal is to introduce new NG clustering algorithms (online and batch) based on the L1 distance (more robust to noise and outliers). We propose three Neural Gas algorithms based on the L1 distance using two different algorithms to find the optimal prototypes and compare them with another well-known clustering algorithm. Given the experiments performed, the proposed methods showed a competitive performance. Preliminary results indicate that research on Neural Gas algorithms based on L1 distance is promising.