PrincipalComponents (filter)
Package
weka.filters.unsupervised.attribute
Synopsis
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.
Options
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. |
Capabilities
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 |