Projection Pursuit Optimization Using LDA Index
LDAopt_Ext(origclass, origdata, q = 1, weight = TRUE, ...)An object of class "PPoptim", which is a list containing:
Numeric value representing the maximum LDA index achieved by the optimal projection. Higher values indicate better class separation.
Numeric matrix of optimal projection coefficients with dimensions
ncol(origdata) by q. Each column represents an optimal projection
direction that maximizes the LDA index for class separation.
The original class information vector passed as input, preserved for reference.
The original data matrix without class information, preserved for reference.
Factor or numeric vector containing the class labels for each observation.
Numeric matrix or data frame containing the predictor variables without class information. Each row represents an observation and each column represents a variable.
Integer specifying the dimension of the projection space. Default is 1 for 1-dimensional projection.
Logical indicating whether to use weighted LDA index calculation.
Default is TRUE.
Additional arguments to be passed to internal optimization methods.
Finds the q-dimensional optimal projection using the Linear Discriminant Analysis (LDA) projection pursuit index. This implementation follows the method described in PPtree.
The LDA projection pursuit index measures class separation by maximizing the ratio of between-class variance to within-class variance in the projected space. This function:
Calls LDAopt to find the optimal q-dimensional projection directions
Evaluates the LDA index for the optimal projection using LDAindex2
Returns both the projection matrix and its associated index value
When weight = TRUE, the index calculation accounts for class proportions,
giving appropriate weight to each class in the optimization.
Lee, EK., Cook, D., Klinke, S., and Lumley, T. (2005) Projection Pursuit for Exploratory Supervised Classification, Journal of Computational and Graphical Statistics, 14(4):831-846.
PDAopt_Ext, findproj_Ext