1 files changed, 21 insertions, 3 deletions
diff --git a/report/paper.md b/report/paper.md
index a31885c..f3bd549 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -173,7 +173,7 @@ can be observed in figure \label{cm}:
 \end{center}
 \end{figure}
 
-Two examples of the outcome of Nearest Neighbor Classification are presented in figures \ref{nn-fail} and \ref{nn-succ},
+Two examples of the outcome of Nearest Neighbor Classification are presented in figures \ref{nn_fail} and \ref{nn_succ},
 respectively one example of classification failure and an example of successful 
 classification.
 
@@ -181,7 +181,7 @@ classification.
 \begin{center}
 \includegraphics[width=7em]{fig/face2.pdf}
 \includegraphics[width=7em]{fig/face5.pdf}
-\label{nn-fail}
+\label{nn_fail}
 \caption{Failure case for NN. Test face left. NN right}
 \end{center}
 \end{figure}
@@ -190,7 +190,7 @@ classification.
 \begin{center}
 \includegraphics[width=7em]{fig/success1.pdf}
 \includegraphics[width=7em]{fig/success1t.pdf}
-\label{nn-fail}
+\label{nn_succ}
 \caption{Success case for NN. Test face left. NN right}
 \end{center}
 \end{figure}
@@ -314,6 +314,24 @@ LDA and it improves recognition performances with respect to PCA and LDA.
 
 # Question 3, LDA Ensemble for Face Recognition, PCA-LDA 
 
+In this section we will perform PCA-LDA recognition with NN classification.
+
+Varying the values of M_pca and M_lda we obtain the average recognition accuracies 
+reported in figure \ref{ldapca_acc}. Peak accuracy of 94.7% can be observed for M_pca=115, M_lda=41;
+howeverer accuracies above 92% can be observed for M_pca values between 90 and 130 and 
+M_lda values between 30 and 50.
+
+Recognition accuracy is significantly higher than PCA, and the run time is roughly the same,
+vaying between 0.11s(low M_pca) and 0.19s(high M_pca).
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=20em]{fig/ldapca3dacc.pdf}
+\caption{PCA-LDA NN Recognition Accuracy varying hyper-parameters}
+\label{ldapca_acc}
+\end{center}
+\end{figure}
+
 # Question 3, LDA Ensemble for Face Recognition, PCA-LDA Ensemble 
 
 # References