1 files changed, 4 insertions, 4 deletions
diff --git a/report/paper.md b/report/paper.md
index 531322e..e94e3a0 100755
--- a/report/paper.md
+++ b/report/paper.md
@@ -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