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.

Fast Self-Organizing Maps Training / Giobergia, Flavio; Baralis, Elena Maria. - ELETTRONICO. - (2019), pp. 2257-2266. ((Intervento presentato al convegno IEEE International Conference on Big Data tenutosi a Los Angeles (USA) nel December 9-12, 2019 [10.1109/BigData47090.2019.9006055].

Fast Self-Organizing Maps Training

GIOBERGIA;BARALIS
2019

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2785833