In the Shogun library, the factor analysis algorithm is implemented in the CFactorAnalysis class. Objects of this class should be configured with the target number of dimensions. The set_target_dim() method should be used to modify the value of the target dimensions, while the fit() and transform() methods should be used for training and data dimensionality reduction, respectively:

 void FAReduction(Some<CDenseFeatures<DataType>> features,
                  const int target_dim) {
   auto fa = some<CFactorAnalysis>();
   fa->set_target_dim(target_dim);
   fa->fit(features);
 
   auto new_features =
       static_cast<CDenseFeatures<DataType> *>(fa->transform(features));
 
   auto feature_matrix = new_features->get_feature_matrix();
   for (index_t i = 0; i < new_features->get_num_vectors(); ++i) {
     auto new_vector = feature_matrix.get_column(i);
   }
 }

The following graph shows the result of applying Shogun factor analysis implementation to our data: