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| -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 5c145c2..da00b3f 100755 --- a/report/paper.md +++ b/report/paper.md @@ -291,7 +291,7 @@ are displayed in table \ref{tab:time}. \begin{figure} \begin{center} \includegraphics[width=17em]{fig/ldapca3dacc.pdf} -\caption{PCA-LDA NN Recognition Accuracy varying hyper-parameters} +\caption{PCA-LDA Accuracy when varying hyper-parameters} \label{fig:ldapca_acc} \end{center} \end{figure} @@ -306,7 +306,7 @@ 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 Confusion Matrix 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} @@ -340,7 +340,7 @@ 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} @@ -416,7 +416,7 @@ 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} |