Distance-Preserving SOM: A New Data Visualization Algorithm

Abstract

As the combination of topology-preserving dimensionality reduction and vector quantization, Self-Organizing Map (SOM) is suitable for visualizing the structure of high-dimensional mass data, which can be used to select more suitable algorithms for subsequent data analysis/processing. However, due to the fixed regular lattice of neurons, SOM has to require some color-coding scheme such as U-matrix to imprint the inter-neuron distance information on the lattice for the aim of visualization. Even so, the structure of the data may often appear in a distorted and unnatural form. In order for the map to visualize the structure of the data faithfully and naturally, the similarity/dissimilarity information should be preserved on the map directly. To do this, a novel variant of SOM, i.e. Distance-Preserving SOM (DPSOM), was presented in this paper. DPSOM can adjust the positions of neurons on the map according to the corresponding distances in the data space, and thus preserve the distance information on the map directly, as Multidimensional Scaling (MDS) does. What’s the most important, DPSOM can automatically avoid the excessive contraction of neurons to one point without any additional parameter, which makes it advantageous over those existing position-adjustable SOMs. Finally, DPSOM can be verified by experimental results well.