From 15a09dd465726f2b685409847810503e9f55f04a Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Tue, 20 Nov 2018 16:43:14 +0000
Subject: Add table of execution time
---
report/paper.md | 19 ++++++++++++++++++-
1 file changed, 18 insertions(+), 1 deletion(-)
(limited to 'report')
diff --git a/report/paper.md b/report/paper.md
index bcb2386..f3b3584 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).
@@ -506,6 +506,23 @@ 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:
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