Linear Discriminant Analysis

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementLinear Discriminant Analysis

Linear Discriminant Analysis (LDA) is similar to PCA but tries to take class information into account to achieve a dimensionality reduction while keeping the class separation high. The result may be used in a subsequent classification. The method tries to maximize the ratio of between-class and within-class scatter in order to achieve a projection where data points of a class are close to each other and far from data points of other classes. More information can be found on Wikipedia.


The number of dimensions to reduce to. This can be at most the number of classes minus one.
Class column
The column containing class information
Column selection
The columns of the original dimensions

Input Ports

The data to reduce the dimensions of

Output Ports

The original data plus columns for the reduced dimensions
The transformation matrix. Can be used with PCA apply.


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