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| -rwxr-xr-x | report/paper.md | 9 |
1 files changed, 6 insertions, 3 deletions
diff --git a/report/paper.md b/report/paper.md index 01984cc..a01c9b2 100755 --- a/report/paper.md +++ b/report/paper.md @@ -73,7 +73,7 @@ and the there is a relation between the eigenvectors obtained: $\boldsymbol{u\te Experimentally there is no consequential loss of data calculating the eigenvectors for PCA when using the low dimensional method. The main advantages of it are reduced computation time, -(since the two methods require on average respectively 3.7s and 0.11s), and complexity of computation +(since the two methods require on average respectively 3.7s and 0.11s from table \ref{tab:time}), and complexity of computation (since the eigenvectors found with the first method are extracted from a significantly bigger matrix). @@ -172,6 +172,7 @@ The alternative method shows overall a better performance (see figure \ref{fig:a for M=5. The maximum M non zero eigenvectors that can be used will in this case be at most the amount of training samples per class minus one, since the same amount of eigenvectors will be used for each generated class-subspace. +A major drawback is the increase in execution time (from table \ref{tab:time}, 1.1s on average). \begin{figure} \begin{center} @@ -285,7 +286,8 @@ howeverer accuracies above 90% can be observed for $M_{\textrm{pca}}$ values bet $M_{\textrm{lda}}$ values between 30 and 50. Recognition accuracy is significantly higher than PCA, and the run time is roughly the same, -vaying between 0.11s(low $M_{\textrm{pca}}$) and 0.19s(high $M_{\textrm{pca}}$). +vaying between 0.11s(low $M_{\textrm{pca}}$) and 0.19s(high $M_{\textrm{pca}}$). Execution times +are displayed in table \ref{tab:time}. \begin{figure} \begin{center} @@ -456,6 +458,7 @@ Seed & Individual$(M=120)$ & Bag + Feature Ens.$(M=60+95)$\\ \hline 1 & 0.929 & 0.942 \\ 5 & 0.897 & 0.910 \\ \hline \end{tabular} +\caption{Comparison of individual and ensemble} \label{tab:compare} \end{table} @@ -508,7 +511,7 @@ From here it follows that AA\textsuperscript{T} and A\textsuperscript{T}A have t \begin{table}[ht] \centering -\begin{tabular}[t]{l|lll} +\begin{tabular}[t]{llll} \hline & Best(s) & Worst(s) & Average(s) \\ \hline PCA & 3.5 & 3.8 & 3.7 \\ |