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diff --git a/report/paper.md b/report/paper.md index 7b46b53..419bdc7 100755 --- a/report/paper.md +++ b/report/paper.md @@ -189,6 +189,18 @@ classification. \end{center} \end{figure} +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 graph below. + +\begin{figure} +\begin{center} +\includegraphics[width=20em]{fig/kneighbors_diffk.pdf} +\caption{NN recognition accuracy varying K. Split: 80-20} +\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 |