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author | nunzip <np.scarh@gmail.com> | 2018-11-20 19:04:07 +0000 |
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committer | nunzip <np.scarh@gmail.com> | 2018-11-20 19:04:07 +0000 |
commit | 4fd68e20c8aba969438c5ce395226bd8c66e2721 (patch) | |
tree | b9359401a7787293bb9deae7ffe710a5ab03796f /report | |
parent | 1e0e0800a64c0adcf13021795fa064e86efd3f74 (diff) | |
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Diffstat (limited to 'report')
-rwxr-xr-x | report/paper.md | 15 |
1 files changed, 7 insertions, 8 deletions
diff --git a/report/paper.md b/report/paper.md index 5c145c2..f1bca34 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} @@ -291,7 +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 hyper-parameters} +\caption{PCA-LDA NN Recognition Accuracy varying Mpca,Mlda} \label{fig:ldapca_acc} \end{center} \end{figure} @@ -306,7 +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 Confusion Matrix Mlda=50, Mpca=115} +\caption{PCA-LDA NN Recognition CM, Mlda=50, Mpca=115} \label{fig:ldapca_cm} \end{center} \end{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} |