# Machine Learning: Models, Challenges, and Research Directions
## 3 Machine Learning Models
### Unsupervised Learning  
Unsupervised machine learning methods analyze and cluster unlabeled outputs using a set of inputs. Clustering of samples is widely used to uncover hidden patterns, structures,  
Figure 4: Classification of semi-supervised learning models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Category** & **Characteristics** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Inductive- \\ Based \\ \end{tabular} } & Generates a model that can create & The predictions of new & The same model can be used \\  & predictions for any sample in the & samples are independent of & in training and predicting new \\  & input space & old samples & data samples \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Transductive- \\ based \\ \end{tabular} } & Predictive strengths are limited to & No difference between the & No distinction between the \\  & objects that are processed during & training and testing steps & transductive algorithms in a \\  & the training steps & trans supervised manner \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics, limitations, and strengths of semi-supervised models [76; 77; 78; 79; 81]. or data knowledge in unlabeled datasets [24; 57]. The following are mathematical concepts of unsupervised models. An unsupervised machine learning model is a parameterized function \(g_{\theta}\) which consists of the input data \(\overrightarrow{x}\in\ X^{d}\) with no outputs. In this model, input data refers to a feature's vector. In addition, \(X^{d}\) is a \(d\)-dimensional vector space. The data have to be trained accurately to predict the data label, which is not known. The steps of unsupervised models are described as follows. Model Training: This process involves the identification of the optimal parameters that can help more accurately find a pattern between data samples and their outputs. So, a training dataset \(D=\left\{\overrightarrow{x}_{i}\right\}_{i=1}^{N}\) with \(N\) number of samples is provided. A loss function is defined to compute the difference between data inputs and the outputs. The aim of training unsupervised models is to diminish the loss function in the training set and achieve the optimal results. Model Prediction: After model training and when optimal parameters have been presented from the input \(\overrightarrow{x}\), the model can discover the inherent structure of unlabeled data, and the prediction accuracy over a testing dataset \(D_{t}\) can be computed to evaluate the performance of the model. Unsupervised models can be divided into cluster-based, dimensionality reduction-based, and neural network-based models, as illustrated in Figure 5. A clustering-based model groups the data into categories based on their characteristics and similarities and clusters them together. This group is capable of finding homogeneous subgroups in the given data, and their results can be used to group observations (features) into distinct groups. These groups help the interpretation and assessment of data. These methods are usually easy to implement; however, their complexity is high [75]. As shown in Figure 5, cluster-based models include K-means, C-means, linear discriminative analysis, hierarchical clustering, non-negative matrix factorization, and density-based spatial clustering with noise. Dimensionality reduction-based models refer to the number of features in a dataset, which are represented as columns. In unsupervised learning, such models aim to decrease the features' number in training and testing. Models like these can summarize relationships between samples with a reduced number of dimensions. Using these approaches facilitates  
Figure 5: Overview of unsupervised learning models. the visualization and preprocessing of complicated datasets. Dimensionality reduction-based models can reduce time and storage, although they may face data loss [75]. AS shown in Figure 5, these categories include self-organization maps, principal component analysis, multidimensional scaling, and independent component analysis.