Algorithm for Data Visualization by Hybridizing Neural Gas Network and Sammons Mapping

Compared with Self-Organizing Feature Map(SOFM), maximum-entropy clustering and K-means clustering, the Neural-Gas network algorithm has advantages of faster convergence, smaller cost distortion errors, etc. However, the fixed and regular neurons on the output space represent worse distance information when the neural gas network algorithm is used for dimension reduction and visualization of linear or nonlinear data sets with nonuniform distribution. Therefore, according to the basic idea of the probabilistic regularized SOFM, a new visualization method for hybridizing neural gas network and Sammons mapping is proposed to overcome this problem, and it reduces the computational complexity with using neural gas network algorithm for feature clustering and preserves the interneuronal distances resemblance from input space into output space by using Sammons mapping. Simulation results show that the proposed hybridizing algorithm can obtain the better visualization effect on the synthetic and real data sets, thus demonstrating the feasibility and effectiveness of the hybridizing algorithm.