Linear Discriminant Analysis

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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.

Options

Dimensions
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

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The data to reduce the dimensions of

Output Ports

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

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