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Performs a principal components analysis and transformation of the data.
Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data – default 0.95 (95%).
Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.


The table below describes the options available for PrincipalComponents.

Option Description
maximumAttributeNames The maximum number of attributes to include in transformed attribute names.
maximumAttributes The maximum number of PC attributes to retain.
normalize Normalize input data.
varianceCovered Retain enough PC attributes to account for this proportion of variance.


The table below describes the capabilites of PrincipalComponents.

Capability Supported
Class Nominal class, Numeric class, Missing class values, Binary class, Date class, No class
Attributes Numeric attributes, Nominal attributes, Unary attributes, Binary attributes, Empty nominal attributes, Date attributes, Missing values
Min # of instances 0

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