In the Shogun library, the IsoMap dimensionality reduction algorithm is implemented in the CIsoMap class. Objects of this class should be configured with the target number of dimensions and the number of neighbors for graph construction. The set_target_dim() and set_k() methods should be used for this. The fit() and transform() methods should be used for the training and data dimensionality reduction, respectively:

void IsoMapReduction(Some<CDenseFeatures<DataType>> features,
                      const int target_dim) {
   auto IsoMap = some<CIsoMap>();
   IsoMap->set_target_dim(target_dim);
   IsoMap->set_k(100);
   IsoMap->fit(features);
 
   auto new_features =
       static_cast<CDenseFeatures<DataType> *>(IsoMap->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 the Shogun IsoMap implementation to our data: