Merge pull request #162 from chhoumann/kb-249-Max-abs-scaler

[KB-249] Max Abs Scaler

report_thesis/src/references.bib

@@ -469,7 +469,6 @@ @article{druckerSVR
 	author = {Drucker, Harris and Burges, Christopher J C and Kaufman, Linda and Smola, Alex J and Vapnik, Vladimir},
 }
 
-
 @article{geurtsERF,
 	title = {Extremely randomized trees},
 	volume = {63},

report_thesis/src/sections/background.tex

@@ -4,8 +4,19 @@ \subsection{Preprocessing}
 \subsubsection{Standard Scaler}
 
 \subsubsection{Max Absolute Scaler}
+Max absolute scaling is a normalization technique that scales each feature individually so that the maximum absolute value of each feature is 1. 
+This results in the data being normalized to a range between -1 and 1.
+The formula for max absolute scaling is given by:
+$$
+	X_{\text{scaled}} = \frac{x}{\max(|x|)},
+$$
+where $x$ is the original feature value and $X_{\text{scaled}}$ is the normalized feature value.
+This scaling method is useful for data that has been centered at zero or data that is sparse, as max absolute scaling does not center the data. 
+This maintains the sparsity of the data by not introducing non-zero values in the zero entries of the data~\cite{Vasques2024}.
+
+
 
-\subsubsection{MinMax Scaler}
+\subsubsection{MinMax Scaler}\label{subsec:minmax_scaler}
 
 \subsubsection{Robust Scaler}
 The robust scaler is a normalization technique that removes the median and scales the data according to the quantile range.