Package

weka.attributeSelection

Synopsis

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.

Options

The table below describes the options available for PrincipalComponents.

Option	Description
maximumAttributeNames	The maximum number of attributes to include in transformed attribute names.
normalize	Normalize input data.
transformBackToOriginal	Transform through the PC space and back to the original space. If only the best n PCs are retained (by setting varianceCovered < 1) then this option will give a dataset in the original space but with less attribute noise.
varianceCovered	Retain enough PC attributes to account for this proportion of variance.

Capabilities

The table below describes the capabilites of PrincipalComponents.

Capability	Supported
Class	Date class, Numeric class, Binary class, Nominal class, Missing class values, No class
Attributes	Numeric attributes, Nominal attributes, Binary attributes, Missing values, Date attributes, Empty nominal attributes, Unary attributes
Min # of instances	1

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