2022
DOI: 10.3390/e24020269
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A Short Review on Minimum Description Length: An Application to Dimension Reduction in PCA

Abstract: The minimun description length (MDL) is a powerful criterion for model selection that is gaining increasing interest from both theorists and practicioners. It allows for automatic selection of the best model for representing data without having a priori information about them. It simply uses both data and model complexity, selecting the model that provides the least coding length among a predefined set of models. In this paper, we briefly review the basic ideas underlying the MDL criterion and its applications… Show more

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Cited by 44 publications
(21 citation statements)
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“…The variables were selected avoiding the redundancy of those that were in perfect correlation or with high collinearity. Subsequently, an analysis was carried out with the PCA statistical method, which allows the simplification through dimension reduction [ 25 ] of the complexity of sample spaces with many dimensions or variables. This model seeks to maximize the variance extracted by the new variables (components).…”
Section: Methodsmentioning
confidence: 99%
“…The variables were selected avoiding the redundancy of those that were in perfect correlation or with high collinearity. Subsequently, an analysis was carried out with the PCA statistical method, which allows the simplification through dimension reduction [ 25 ] of the complexity of sample spaces with many dimensions or variables. This model seeks to maximize the variance extracted by the new variables (components).…”
Section: Methodsmentioning
confidence: 99%
“…Multivariate statistical analysis PCA is a widely used complex technique to reduce the dimension of multivariate problems. It reduces the dimension of the original data set by resolving correlations between a large number of variables with a smaller number of potential factors, with minimal information loss [31]. We performed a preliminary analysis of the MS datasets using PCA and viewed grouping trends of different processed products.…”
Section: 31mentioning
confidence: 99%
“…Principal component analysis (PCA) consisted of expressing a collection of variables in a set of linear combinations of factors not correlated with each other [12,38]. This method allowed the original data (individuals and variables) to be represented in a space with a dimension less than the original space [12,38].…”
Section: Procedures For Quantitative Study Specimens By Principal Com...mentioning
confidence: 99%