Visualizing the distribution of a large-scale pattern set using compressed relative neighborhood graph

The goal of this research is to understand the true distribution of character patterns. Advances in computer technology for mass storage and digital processing have paved way to process a massive dataset for various pattern recognition problems. If we can represent and analyze the distribution of a large-scale pattern set directly and understand its relationships deeply, it should be helpful for improving classifier for pattern recognition. For this purpose, we use a visualization method to represent the distribution of patterns using a relative neighborhood graph (RNG), where each node corresponds to a single pattern. Specifically, we visualize the pattern distribution using a compressed representation of RNG (Clustered-RNG). Clustered-RNG can visualize inter-class relationships (e.g. neighboring relationships and overlaps of pattern distribution among "multiple classes") and it represents the distribution of the patterns without any assumption, approximation or loss. Through large-scale printed and handwritten digit pattern experiments, we show the properties and validity of the visualization using Clustered-RNG.