Selección del modelo por criterios de información para análisis de componentes principales ocultas de markov


Authors

  • Mauricio Alvarez
  • Ricardo Henao

Abstract

El modelo de análisis de componentes principales ocultas de Markov tiene en cuenta la dependencia entre observaciones multivariadas, que en el análisis tradicional de componentes principales se omite por completo. Para la aplicación de este modelo dinámico, es necesario conocer la dimensión de los subespacios principales asociados a cada estado de una cadena de Markov y el número de estados en la cadena. En este artículo, se emplean criterios de información para seleccionar los parámetros del modelo y se presenta una estrategia secuencial de selección de parámetros que reduce el costo computacional. Se muestran resultados utilizando bases de datos sintéticas

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Published

2008-06-30

How to Cite

Alvarez, M., & Henao, R. (2008). Selección del modelo por criterios de información para análisis de componentes principales ocultas de markov. Scientia Et Technica, 1(38). Retrieved from https://ojs2.utp.edu.co/index.php/revistaciencia/article/view/3689

Issue

Vol. 1 No. 38 (2008)

Section

Eléctrica

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