Self-Organizing Maps
Also known as SOM, MAP
Overview
Self-Organizing Maps project high-dimensional examples onto a low-dimensional grid, updating each best-matching unit and its neighbors so nearby inputs tend to remain nearby on the map. Variants include growing and hierarchical maps, SOM-based vector quantization, and optimized or GPU- and distributed implementations for large, sparse, and streaming datasets. They are used for exploratory analysis and as components in tasks such as continual learning, local conformal calibration, and representation learning, with performance assessed through measures such as quantization error and neighborhood preservation as well as task-specific benchmarks. A central tension is scaling and adapting maps without losing useful topology; moreover, preserving structure does not guarantee fair representations, since maps can reveal sensitive attributes absent from training labels.
Latest papers
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