# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Feature Reduction
#### 2.3.2 Feature Extraction  
Feature extraction performs a transformation of the original variables to generate other features using the mapping function \(F\) that preserves most of the relevant information. This transformation can be achieved by a linear or non-linear combination of original features. For example, for \(n\) features \(f_{1},f_{2},f_{3},\ldots,f_{n}\), we extract a new feature set \(f_{1}^{\prime},f_{2}^{\prime},f_{3}^{\prime},\ldots,f_{m}^{\prime}\) where \(m<n\) and \(f_{i}^{\prime}=F(f_{1},f_{2},f_{3},\ldots,f_{n})\). With the feature extraction technique, the feature space can often be decreased without losing a lot of information on the original attribute space. However, one of its limits is that the information about how the initial features contribute is often lost [52]. Moreover, it is difficult to find a relationship between the original features and the new features. Therefore, the analysis of the new features is almost impossible, since no physical meaning for the transformed features is obtained from feature extraction techniques. In this survey, we discuss three of the most frequently used methods.  
_Principal Component Analysis (PCA)_ is the oldest technique of multivariate analysis and was introduced by [PERSON] in 1901. It is an unsupervised (it does not take into account target variable) and non-parametric technique that reduces the dimensionality of data from \(f\) to \(p\), where \(p<f\), by transforming the initial feature space into a smaller space. However, PCA has some limitations, discussed below:  
* It assumes that the relations between variables are linear;
* It depends on the scaling of the data (i.e., each variable is normalized to zero mean);
* We do not know how many PCs should be retained (the optimal number of principal components (PCs));
* It does not consider the correlation between target outputs and input features [23];  
_Autoencoder (AE)_ is an unsupervised learning model which seeks to reconstruct the input from the hidden layer [68]. During the process, the AE tries to minimize the reconstruction error by improving the quality of the learned feature. It is frequently used to learn discriminative features of original data. Hence, AE is potentially important for feature extraction, and many researchers use it to generate reduced feature sets (code). In many cases, deep autoencoder (DAE) outperforms conventional feature selection methods and PCA [69] since it consists of several layers with nonlinear activation functions to extract features intelligently;  
_Linear discriminant analysis (LDA)_ is a feature extraction method used as a preprocessing step for ML algorithms and was first proposed in [70]. It is similar to PCA, but LDA is a supervised method. In other words, it takes the target variable into account. It consists of three main steps, as mentioned in [71]. Firstly, it calculates the _between-class variance_ using the distance between the means of the classes. Secondly, it calculates the _within-class variance_ using the distance between the mean and the observations of each class. Finally, it constructs the lower dimensional space, which maximizes the between-class variance and minimizes the within-class variance.