A distance metric learning algorithm for supervised dimensionality reduction, maximizing the ratio of variances between classes and within classes.
LDA(num_dims = NULL, thres = NULL)
| num_dims | Number of components (< n_classes - 1) for dimensionality reduction. If None, it will be taken as n_classes - 1. Ignored if thres is provided. Integer. |
|---|---|
| Fraction | of variability to keep, from 0 to 1. Data dimension will be reduced until the lowest dimension that keeps 'thres' explained variance. Float. |
The LDA transformer, structured as a named list.