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Diffstat (limited to 'report')
| -rwxr-xr-x[-rw-r--r--] | report/makefile | 0 | ||||
| -rwxr-xr-x | report/paper.md | 19 |
2 files changed, 18 insertions, 1 deletions
diff --git a/report/makefile b/report/makefile index afbac8d..afbac8d 100644..100755 --- a/report/makefile +++ b/report/makefile diff --git a/report/paper.md b/report/paper.md index ef1e9c4..01984cc 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 3.4s and 0.11s), and complexity of computation +(since the two methods require on average respectively 3.7s and 0.11s), and complexity of computation (since the eigenvectors found with the first method are extracted from a significantly bigger matrix). @@ -504,6 +504,23 @@ 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]{l|lll} +\hline + & Best(s) & Worst(s) & Average(s) \\ \hline +PCA & 3.5 & 3.8 & 3.7 \\ +PCA-F & 0.10 & 0.24 & 0.11 \\ +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} + ## Code All code and \LaTeX sources are available at: |