Comprehensive Visualization of Multi-View Relational Data by Self-Organizing Maps

Abstract

The purpose of this research is to develop a self-organizing map (SOM) which comprehensively visualizes the overall picture of multi-view relational data. In order to realize this, we extended SOM to multi-view data and made it possible to estimate the factors common to all views. This algorithm is regarded as a nonlinear extension of canonical correlation analysis by SOM. Furthermore, we tried to incorporate the developed multi-view learning algorithm into the SOM for relational data. We applied our method to wine data analysis and showed its usefulness.