Fast Self-Organizing Maps Training

Self-organizing maps are an unsupervised machine learning technique that offers interpretable results by identifying topological properties in high-dimensional datasets and projecting them on a 2-dimensional grid. An important problem of self-organizing maps is the computational expensiveness of their training phase. In this paper, we propose a fast approach to train self-organizing maps. The approach consists of 2 steps. First, a small map identifies the most relevant areas from the entire high-dimensional input space. Then a larger map (initialized from the small one) is fine-tuned to further explore the local areas identified in the first step. The resulting map has performance (measured in terms of accuracy and quantization error) on par with self-organizing maps trained with the standard approach, but with a significantly reduced training time.