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| -rwxr-xr-x | report/paper.md | 11 |
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} |