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authornunzip <np.scarh@gmail.com>2018-11-16 17:55:17 +0000
committernunzip <np.scarh@gmail.com>2018-11-16 17:55:17 +0000
commit6ecc84b2081e2ec9da71e774a273cb668baa7140 (patch)
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parent702cbeec081f884d336c5b61b8717b9df5b4c48b (diff)
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Remove useless figure1. Correct small mistakes
-rwxr-xr-xreport/paper.md28
1 files changed, 10 insertions, 18 deletions
diff --git a/report/paper.md b/report/paper.md
index 6291273..8dc0421 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -12,15 +12,8 @@ accuracy is obtained when using a 90% of the data for
training. Despite such results we will be using 70% of the data
for training as a standard. This will allow to give more than one
example of success and failure for each class when classifying the
-test_data.
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=20em]{fig/partition.pdf}
-\label{accuracy}
-\caption{NN Recognition Accuracies for different data partitions}
-\end{center}
-\end{figure}
+test_data. Moreover using 90% training data would make the results
+obtained heavilly dependent on the seed chosen.
After partitioning the data into training and testing sets,
PCA is applied. The covariance matrix, S, of dimension
@@ -118,7 +111,7 @@ From here it follows that AA\textsuperscript{T} and A\textsuperscript{T}A have t
It can be noticed that we effectively don't lose any data calculating the eigenvectors
for PCA with the second method. The main advantages of it are in terms of speed,
-(since the two methods require on average respectively 3.4s and 0.14s), and complexity of computation
+(since the two methods require on average respectively 3.4s and 0.11s), and complexity of computation
(since the eigenvectors found with the first method are extracted from a significantly
bigger matrix).
@@ -131,7 +124,7 @@ the covariance matrix, whereas method 2 requires an additional projection step.
Using the computational method for fast PCA, face reconstruction is then performed.
The quality of reconstruction will depend on the amount of eigenvectors picked.
-The results of varying M can be observed in the picture in fig.\ref{face160rec}. Two faces from classes
+The results of varying M can be observed in fig.\ref{face160rec}. Two faces from classes
number 21 and 2 respectively, are reconstructed as shown in fig.\ref{face10rec} with respective M values
of M=10, M=100, M=200, M=300. The last picture is the original face.
@@ -198,7 +191,7 @@ classification.
It is possible to use a NN classification that takes into account majority voting.
With this method recognition is based on the K closest neighbors of the projected
test image. Such method anyways showed the best recognition accuracies for PCA with
-K=1, as it can be observed from the figure \ref{k-diff}.
+K=1, as it can be observed from figure \ref{k-diff}.
\begin{figure}
\begin{center}
@@ -237,7 +230,6 @@ can be observed in figure \ref{cm-alt}.
\end{figure}
Similarly to the NN case, we present two cases, respectively failure and success.
-The pictures on the right show the reconstructed images.
\begin{figure}
\begin{center}
@@ -281,7 +273,7 @@ $$ S\textsubscript{W} = \sum\limits_{c}\sum\limits_{i\in c}(x\textsubscript{i} -
To maximize J(W) we differentiate with respect to W and equate to zero:
-$$ \frac{d}{dW}J(W) = \frac{d}{dW}(\frac{W\textsuperscript{T}S\textsubscript{B}W}{W\textsuperscript{T}S\textsubscript{W}W}) = 0 $$
+$$ \frac{d}{dW}J(W) = \frac{d}{dW}\left(\frac{W\textsuperscript{T}S\textsubscript{B}W}{W\textsuperscript{T}S\textsubscript{W}W}\right) = 0 $$
$$ (W\textsuperscript{T}S\textsubscript{W}W)\frac{d(W\textsuperscript{T}S\textsubscript{B}W)}{dW} - (W\textsuperscript{T}S\textsubscript{B}W)\frac{d(W\textsuperscript{T}S\textsubscript{W}W)}{dW} = 0 $$
$$ (W\textsuperscript{T}S\textsubscript{W}W)2S\textsubscript{B}W - (W\textsuperscript{T}S\textsubscript{B}W)2S\textsubscript{W}W = 0 $$
$$ S\textsubscript{B}W - JS\textsubscript{W}W = 0 $$
@@ -291,15 +283,15 @@ $$ S\textsubscript{W}\textsuperscript{-1}S\textsubscript{B}W - JW = 0 $$
From here it follows:
-$$ W\textsubscript{opt} = arg\underset{W}max|\frac{W\textsuperscript{T}S\textsubscript{B}W}{W\textsuperscript{T}S\textsubscript{W}W}| = S\textsubscript{W}\textsuperscript{-1}(\mu\textsubscript{1} - \mu\textsubscript{2}) $$
+$$ W\textsubscript{opt} = arg\underset{W}max\frac{|W\textsuperscript{T}S\textsubscript{B}W|}{|W\textsuperscript{T}S\textsubscript{W}W|} = S\textsubscript{W}\textsuperscript{-1}(\mu\textsubscript{1} - \mu\textsubscript{2}) $$
However S\textsubscript{W} is often singular since the rank of S\textsubscript{W}
is at most N-c and usually N is smaller than D.
In such case it is possible to use Fisherfaces. The optimal solution to such
-problem lays in W\textsuperscript{T}\textsubscript{opt} = W\textsuperscript{T}\textsubscript{lda}W\textsuperscript{T}\textsubscript{pca}
+problem lays in W\textsuperscript{T}\textsubscript{opt} = W\textsuperscript{T}\textsubscript{lda}W\textsuperscript{T}\textsubscript{pca},
-Where W\textsubscript{pca} is chosen to maximize the determinant of the total scatter matrix
+where W\textsubscript{pca} is chosen to maximize the determinant of the total scatter matrix
of the projected samples: $$ W\textsuperscript{T}\textsubscript{pca} = arg\underset{W}max|W\textsuperscript{T}S\textsubscript{T}W| $$
$$ And $$
$$ W\textsubscript{lda} = arg\underset{W}max\frac{|W\textsuperscript{T}W\textsuperscript{T}\textsubscript{pca}S\textsubscript{B}W\textsubscript{pca}W|}{|W\textsuperscript{T}W\textsuperscript{T}\textsubscript{pca}S\textsubscript{W}W\textsubscript{pca}W|} $$
@@ -332,7 +324,7 @@ vaying between 0.11s(low M_pca) and 0.19s(high M_pca).
\end{center}
\end{figure}
-DD RANK OF SCATTER MATRICES
+ADD RANK OF SCATTER MATRICES
Testing with M_lda=50 and M_pca=115 gives 92.9% accuracy. The results of such test can be
observed in the confusion matrix shown in figure \ref{ldapca_cm}.