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authorVasil Zlatanov <v@skozl.com>2018-11-20 19:17:50 +0000
committerVasil Zlatanov <v@skozl.com>2018-11-20 19:17:50 +0000
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Change description of altenative method
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@@ -162,10 +162,10 @@ K=1, as visible in figure \ref{fig:k-diff}.
\end{center}
\end{figure}
-The process for alternative method is somewhat similar to LDA. One different
-subspace is generated for each class. These subspaces are then used for reconstruction
-of the test image and the class of the subspace that generated the minimum reconstruction
-error is assigned.
+The process for alternative method is draws similarities to LDA. Similarly to LDA it calculates per class means. It then projects
+images onto eigenvectors from subspaces generated per each class. While it does not attempt to discriminate features per class, the
+calculation of independent class subspaces is effective at differentiating between the classes when reconstruction error from each class
+subspace is compared. The class with the subspace that generates the least error is selected as the label.
The alternative method shows overall a better performance (see figure \ref{fig:altacc}), with peak accuracy of 69%
for $M=5$. The maximum $M$ non zero eigenvectors that can be used will in this case be at most