1 files changed, 4 insertions, 4 deletions
diff --git a/report/paper.md b/report/paper.md
index f1bca34..7c91d0f 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -290,7 +290,7 @@ are displayed in table \ref{tab:time}.
 \begin{figure}
 \begin{center}
 \includegraphics[width=17em]{fig/ldapca3dacc.pdf}
-\caption{PCA-LDA NN Recognition Accuracy varying Mpca,Mlda}
+\caption{PCA-LDA Accuracy when varying hyper-parameters}
 \label{fig:ldapca_acc}
 \end{center}
 \end{figure}
@@ -305,7 +305,7 @@ Testing with $M_{\textrm{lda}}=50$ and $M_{\textrm{pca}}=115$ gives 92.9% accura
 \begin{figure}
 \begin{center}
 \includegraphics[width=17em]{fig/cmldapca.pdf}
-\caption{PCA-LDA NN Recognition CM, Mlda=50, Mpca=115}
+\caption{PCA-LDA Recognition Confusion Matrix $M_{textrm{lda}}=50$, $M_{\textrm{pca}}=115$}
 \label{fig:ldapca_cm}
 \end{center}
 \end{figure}
@@ -339,7 +339,7 @@ the 3 features of the subspaces obtained are graphed.
 \begin{center}
 \includegraphics[width=12em]{fig/SubspaceQ1.pdf}
 \includegraphics[width=12em]{fig/SubspaceQL1.pdf}
-\caption{Generated Subspaces (3 features). PCA on the left. PCA-LDA on the right}
+\caption{Subspace with 3 features. PCA on left. PCA-LDA on right}
 \label{fig:subspaces}
 \end{center}
 \end{figure}
@@ -415,7 +415,7 @@ The optimal number of constant and random eigenvectors to use is therefore an in
 \begin{figure}
 \begin{center}
 \includegraphics[width=19em]{fig/vaskplot3.pdf}
-\caption{Recognition accuracy varying M and Randomness Parameter}
+\caption{Accuracy when varying M and Randomness Parameter}
 \label{fig:opti-rand}
 \end{center}
 \end{figure}