From b5d3bed9a9548c80eb457b61fb5ff33de3f77a8c Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Thu, 13 Dec 2018 16:51:58 +0000 Subject: s/label/ref/ --- report/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/report/paper.md b/report/paper.md index 2789923..a20e943 100755 --- a/report/paper.md +++ b/report/paper.md @@ -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. -- cgit v1.2.3