Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction

Abstract

Machine learning such as self-organizing feature maps (SOMs) is a commonly used technique for clustering and data dimensionality reduction. In fact, their inherent property of topology preservation and unsupervised learning of processed data put them in the front of candidates for data reduction. However, the high computational cost of SOMs limits their use to offline approaches and makes the on-line real-time high-performance SOM processing more challenging. This chapter focus on the large-scale distributed and scalable SOM model adapted for distributed computing nodes and present the main challenges for its adoption in the resources limited environments.