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authorVasil Zlatanov <v@skozl.com>2018-12-13 16:51:58 +0000
committerVasil Zlatanov <v@skozl.com>2018-12-13 16:51:58 +0000
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@@ -71,7 +71,7 @@ identification is shown in red.
Magnitude normalization is a common technique, used to equalize feature importance.
Applying magnitude normalization (scaling feature vectors to unit length) had a negative
effect re-identification. Furthemore standartization by removing feature mean and deviation
-also had negative effect on performance as seen on figure \label{fig:baselineacc}. This may
+also had negative effect on performance as seen on figure \ref{fig:baselineacc}. This may
be due to the fact that we are removing feature scaling that was introduced by the Neural network,
such that some of the features are more significant than the others. By standartizing our
features at this point, we remove such scaling and may be losing using useful metrics.