Essentials of the self-organizing map

By Teuvo Kohonen. 2013

1,511 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Bi-Objective Continual Learning: Learning 'New' While Consolidating 'Known' (2020, methodology): “To achieve this purpose, we try to map high-dimensional F i to low-dimensional P i using a self-organizing map (SOM) (Kohonen 2013).”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “They are applied to a vast range of application fields going from industry, finance, natural sciences, to linguistics [87].”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, background): “SOM is widely utilized in a variety of applications, including pattern identification, health or medical diagnosis, anomaly detection, and virus or worm attack detection [60] [87].”
Machine learning for data-driven discovery in solid Earth geoscience (2019, methodology): “A SOM is a type of unsupervised neural network that can be used for either dimensionality reduction or clustering (80) [see Roden et al.”
Spatial expression of transcription factors in Drosophila embryonic organ development (2013, methodology): “The SOM algorithm reduces the complexity of the high dimensional organ system expression matrix by placing the TFs onto a two-dimensional (2D) surface with a clustering procedure similar to k-means, while adjusting the surface to the shape of the TF data cloud (reviewed in [29]).”
Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes (2015, background): “Another reason for many stimuli at the side being mapped to the two outermost neurons is the border effect observable in other SOM-like algorithms (Kohonen, 2001, 2013).”
Class-Incremental Learning with Topological Schemas of Memory Spaces (2021, methodology): “The parameters of μ k and the adjacent mean vectors of μ t+1 k are updated with the Hebbian rule [38].”
A review of learning in biologically plausible spiking neural networks (2019, background): “In a basic SOM the neighbourhood Gaussian function has a time varying width which is decreased by the learning epochs.”
Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, neutral): “SOMs are versatile for analyzing nonlinear projections, multivariate and complex data sets [227] .”