Selección de dimensionalidad en análisis de componentes principales utilizando modelos bayesianos


Authors

  • Ricardo Henao
  • Jorge Hernándo Rivera

Abstract

El gran inconveniente del análisis de componentes principales (PCA) es la correct elección del número componentes que deben ser retenidas, debido a esto, en este artículo se presenta un análisis experimental de varias técnicas de selección de dimensionalidad automáticas para PCA, basadas en dos variantes denominadas Análisis de Componentes Principales Probabilístico (PPCA) y nálisis Variacional de Componentes Principales (VPCA). Los métodos de empleados en este trabajo están fundamentados en la selección bayesiana del modelo y los criterios de información. En los resultados obtenidos, el método que aplica Laplace obtiene mejores resultados globales con un costo computacional satisfactorio.

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Published

2005-01-10

How to Cite

Henao, R., & Rivera, J. H. (2005). Selección de dimensionalidad en análisis de componentes principales utilizando modelos bayesianos. Scientia Et Technica, 2(28). Retrieved from https://ojs2.utp.edu.co/index.php/revistaciencia/article/view/6817

Issue

Vol. 2 No. 28 (2005)

Section

Eléctrica

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