MS-SOM: Magnitude Sensitive Self-Organizing Maps
Conference paper
Abstract
This paper presents a new neural algorithm, MS-SOM, as an extension of SOM, that maintaining the topological representation of stimulus also introduces a second level of organization of neurons. MS-SOM units tend to focus the learning process in data space zones with high values of a user-defined magnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use of a learning factor, evaluated locally, for each unit. Some results in several examples demonstrate the better performance of MS-SOM compared to SOM.
Keywords
Self-Organizing Maps Magnitude Sensitive Competitive learning unsupervised learning classification surface modellingPreview
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