linear_discriminant_analysis.rst

Linear Discriminant Analysis

This cookbook page introduces the application of linear discriminant analysis to multi-class classifications.

Example

Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and CMulticlassLabels as

linear_discriminant_analysis.sg:create_features

We create an instance of the CMCLDA classifier with feature matrix and label list. CMCLDA also has two default parameters, to set tolerance used in training and mark whether to store the within class covariances.

linear_discriminant_analysis.sg:create_instance

Then we train and apply it to the test data, which here gives CMulticlassLabels.

linear_discriminant_analysis.sg:train_and_apply

We can extract the mean vector of one class. If we enabled storing covariance when creating instances, we can also extract the covariance matrix:

linear_discriminant_analysis.sg:extract_mean_and_cov

We can evaluate test performance via e.g. CMulticlassAccuracy.

linear_discriminant_analysis.sg:evaluate_accuracy

References

Linear_discriminant_analysis

Linear_discriminant_analysis#Multiclass_LDA