Description Usage Arguments Value
A distance metric learning algorithm for supervised dimensionality reduction, maximizing the ratio of variances between classes and within classes.
1 |
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.
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