Probabilistic sequential independent components analysis.
IEEE Trans Neural Netw
; 15(4): 838-49, 2004 Jul.
Article
en En
| MEDLINE
| ID: mdl-15461077
ABSTRACT
Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje por Probabilidad
/
Algoritmos
/
Inteligencia Artificial
/
Modelos Estadísticos
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Técnicas de Apoyo para la Decisión
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Redes Neurales de la Computación
/
Teoría de la Información
Tipo de estudio:
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw
Asunto de la revista:
INFORMATICA MEDICA
Año:
2004
Tipo del documento:
Article
País de afiliación:
Canadá