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Titolo
Beta Scale Invariant Map
Autor(es)
Soggetto
Computer Science
Fecha de publicación
2017
Editore
Elsevier
Citación
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. Volumen 59, pp. 218–235. Elsevier.
Resumen
In this study we present a novel version of the Scale Invariant Map (SIM) called Beta-SIM, developed to facilitate the clustering and visualization of the internal structure of complex datasets effectively and efficiently. It is based on the application of a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution, when applied to the Scale Invariant Map. The Beta-SIM behavior is thoroughly analyzed and successfully demonstrated over 2 artificial and 16 real datasets, comparing its results, in terms of three performance quality measures with other well-known topology preserving models such as Self Organizing Maps (SOM), Scale Invariant Map (SIM), Maximum Likelihood Hebbian Learning-SIM (MLHL-SIM), Visualization Induced SOM (ViSOM), and Growing Neural Gas (GNG). Promising results were found for Beta-SIM, particularly when dealing with highly complex datasets.
URI
ISSN
0952-1976
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