diff options
| -rwxr-xr-x | report/paper.md | 8 |
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} |