List of usage examples for weka.attributeSelection UnsupervisedAttributeEvaluator subclass-usage
From source file PrincipalComponents.java
/**
* <!-- globalinfo-start --> Performs a principal components analysis and
* transformation of the data. Use in conjunction with a Ranker search.
* Dimensionality reduction is accomplished by choosing enough eigenvectors to
* account for some percentage of the variance in the original data---default
* 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,
From source file data.generation.target.utils.PrincipalComponents.java
/**
<!-- globalinfo-start -->
* Performs a principal components analysis and transformation of the data. Use in conjunction with a Ranker search. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data---default 0.95 (95%). Attribute noise can be filtered by transforming to the PC space, eliminating some of the worst eigenvectors, and then transforming back to the original space.
* <p/>
<!-- globalinfo-end -->
*
From source file HomeWork7.PrincipalComponents.java
/**
* <!-- globalinfo-start --> Performs a principal components analysis and
* transformation of the data. Use in conjunction with a Ranker search.
* Dimensionality reduction is accomplished by choosing enough eigenvectors to
* account for some percentage of the variance in the original data---default
* 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,
From source file hw7.PrincipalComponents.java
/**
* <!-- globalinfo-start --> Performs a principal components analysis and
* transformation of the data. Use in conjunction with a Ranker search.
* Dimensionality reduction is accomplished by choosing enough eigenvectors to
* account for some percentage of the variance in the original data---default
* 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,