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-rwxr-xr-xreport/paper.md17
1 files changed, 5 insertions, 12 deletions
diff --git a/report/paper.md b/report/paper.md
index 134243b..1df9335 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -156,8 +156,8 @@ K=1, as visible in figure \ref{fig:k-diff}.
\begin{figure}
\begin{center}
-\includegraphics[width=17em]{fig/kneighbors_diffk.pdf}
-\caption{NN recognition accuracy varying K. Split: 80-20}
+\includegraphics[width=19em]{fig/kneighbors_diffk.pdf}
+\caption{NN Accuracy varying K. Split: 80-20}
\label{fig:k-diff}
\end{center}
\end{figure}
@@ -277,8 +277,6 @@ after each step.
# Question 3, LDA Ensemble for Face Recognition, PCA-LDA
-In this section we will perform PCA-LDA recognition with NN classification.
-
Varying the values of $M_{\textrm{pca}}$ and $M_{\textrm{lda}}$ we obtain the average recognition accuracies
reported in figure \ref{fig:ldapca_acc}. Peak accuracy of 93% can be observed for $M_{\textrm{pca}}=115$, $M_{\textrm{lda}}=41$;
howeverer accuracies above 90% can be observed for $130 > M_{\textrm{pca}} 90$ and $50 > M_{\textrm{lda}} > 30$ values between 30 and 50.
@@ -289,7 +287,7 @@ are displayed in table \ref{tab:time}.
\begin{figure}
\begin{center}
-\includegraphics[width=17em]{fig/ldapca3dacc.pdf}
+\includegraphics[width=20em]{fig/ldapca3dacc.pdf}
\caption{PCA-LDA Accuracy when varying hyper-parameters}
\label{fig:ldapca_acc}
\end{center}
@@ -376,7 +374,7 @@ Bagging is performed by generating each dataset for the ensembles by randomly pi
\begin{figure}
\begin{center}
-\includegraphics[width=22em]{fig/bagging.pdf}
+\includegraphics[width=20em]{fig/bagging.pdf}
\caption{Ensemble size effect on accuracy with bagging}
\label{fig:bagging-e}
\end{center}
@@ -392,7 +390,7 @@ use the 90 eigenvectors with biggest variance and picking 70 of the rest non-zer
\begin{figure}
\begin{center}
-\includegraphics[width=23em]{fig/random-ensemble.pdf}
+\includegraphics[width=21em]{fig/random-ensemble.pdf}
\caption{Ensemble size - feature randomisation ($m_c=90$,$m_r=70$)}
\label{fig:random-e}
\end{center}
@@ -509,8 +507,6 @@ We know that $S\boldsymbol{u\textsubscript{i}} = \lambda \textsubscript{i}\bolds
From here it follows that AA\textsuperscript{T} and A\textsuperscript{T}A have the same eigenvalues and their eigenvectors follow the relationship $\boldsymbol{u\textsubscript{i}} = A\boldsymbol{v\textsubscript{i}}$
-### Table of execution times of different methods
-
\begin{table}[ht]
\centering
\begin{tabular}[t]{llll}
@@ -522,12 +518,9 @@ PCA-ALT & 1.0 & 1.3 & 1.1 \\
LDA & 5.0 & 5.8 & 5.2 \\
LDA-PCA & 0.11 & 0.19 & 0.13 \\ \hline
\end{tabular}
-\caption{Comparison of execution times between different methods}
\label{tab:time}
\end{table}
-### Memory Usage for NN and alternative method
-
\begin{figure}
\begin{center}
\includegraphics{fig/memnn.pdf}