Shiliang Sun, Xijiong Xie and Mo Yang. Multi-view Uncorrelated Discriminant Analysis, IEEE Transaction on Cybernetics.
This code can be a running on multiple-feature dataset. It contains two files.
1. One file has four multi-view linear dimension reduction methods which are MLDA, MULDA, MLDA-m and MULDA-m.
  The file multifeat is multiple-feature dataset. 
  The file knn_classify is one code of KNN classifier. 
  The file main_5fold_MULDA is used to select the optimal parameters of MLDA and MULDA. 
  The file main_5fold_MULDAm is used to select the optimal parameters of MLDA-m and MULDA-m. 
  The file main_realsimp_MULDA is used to obtain the final results of MLDA and MULDA.
  The file main_realsimp_MULDA_m is used to obtain the final results of MLDA-m and MULDA-m.   
2. The other one has five multi-view nonlinear dimension reduction methods which are KDCCA, KMDA, KMDA-m, KMUDA and KMUDA-m.
  The file multifeat is multiple-feature dataset.
  The file knn_classify is one code of KNN classifier. 
  The file main_5fold_KMUDA is used to select the optimal parameters of KDCCA, KMDA and KMUDA.
  The file main_5fold_KMUDAm is used to select the optimal parameters of KMDA-m and KMUDA-m.
  The file main_simp_KMUDA is used to obtain the final results of KDCCA£¬KMDA and KMUDA.
  The file main_simp_KMUDAm is used to obtain the final results of KMDA-m and KMUDA-m.