Added missing citations

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -188,9 +188,9 @@ allows a probabilistic interpretation. Pidhorskyi et al.~\cite{Pidhorskyi2018}
 combine a probabilistic approach to novelty detection with auto-encoders.
 
 Distance-based novelty detection uses either nearest neighbour-based approaches
-(e.g. ) %TODO citations)
+(e.g. \(k\)-nearest neighbour \cite{Hautamaki2004})
 or clustering-based approaches
-(e.g. ). % TODO citations
+(e.g. \(k\)-means clustering algorithm \cite{Jordan1994}).
 Both methods are similar to estimating the
 pdf of data, they use well-defined distance metrics to compute the distance
 between two data points.
@@ -198,7 +198,7 @@ between two data points.
 Domain-based novelty detection describes the boundary of the known data, rather
 than the data itself. Unknown data is identified by its position relative to
 the boundary. A common implementation for this are support vector machines
-(e.g. implemented by ). % TODO citations
+(e.g. implemented by Song et al. \cite{Song2002}).
 
 Information-theoretic novelty detection computes the information content
 of a data set, for example, with metrics like entropy. Such metrics assume
@@ -206,8 +206,8 @@ that novel data inside the data set significantly alters the information
 content of an otherwise normal data set. First, the metrics are calculated over the
 whole data set. Afterwards, a subset is identified that causes the biggest
 difference in the metric when removed from the data set. This subset is considered
-to consist of novel data. For example, xyz provide a recent approach.
-% TODO citations
+to consist of novel data. For example, Filippone and Sanguinetti \cite{Filippone2011} provide
+a recent approach.
 
 \subsection{Reconstruction-based novelty detection}