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-rwxr-xr-xreport/paper.md11
1 files changed, 5 insertions, 6 deletions
diff --git a/report/paper.md b/report/paper.md
index da00b3f..7c91d0f 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -114,9 +114,8 @@ The analysed classification methods used for face recognition are Nearest Neighb
alternative method utilising reconstruction error.
Nearest Neighbor projects the test data onto the generated subspace and finds the closest
-training sample to the projected test image, assigning the same class as that of the nearest neighbor.
-
-Recognition accuracy of NN classification can be observed in figure \ref{fig:accuracy}.
+training sample to the projected test image, assigning the same class as that of the nearest neighbor. Recognition accuracy
+of NN classification can be observed in figure \ref{fig:accuracy}.
A confusion matrix showing success and failure cases for Nearest Neighbor classification when using PCA can be observed in figure \ref{fig:cm}:
@@ -184,7 +183,7 @@ memory associated with storing the different eigenvectors is deallocated, the to
\end{center}
\end{figure}
-A confusion matrix showing success and failure cases for alternative method classification
+A confusion matrix showing success and failure cases for alternative method
can be observed in figure \ref{fig:cm-alt}.
\begin{figure}
@@ -531,8 +530,8 @@ LDA-PCA & 0.11 & 0.19 & 0.13 \\ \hline
\begin{figure}
\begin{center}
-\includegraphics[width=15em]{fig/memnn.pdf}
-\includegraphics[width=15em]{fig/memalt.pdf}
+\includegraphics{fig/memnn.pdf}
+\includegraphics{fig/memalt.pdf}
\caption{Memory Usage for NN and alternative method}
\label{fig:mem}
\end{center}