**4. Conclusions**

This chapter introduces recent methods for processing missing values. Besides, four types of commonly used algorithms, namely, *K*-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches, were examined. Their advantages and disadvantages were also discussed in each subsection. It is worth noting that data imputation usually does not require training data. It becomes impractical when data imputation needs supervisory information or the ground truth (notably, the ground truth is unobservable). This is because when missing values occur in training data and even when the ground truth is missing, the supervised methods even cannot work to learn the ground truth. Therefore, those selected four types of commonly used algorithms in this chapter did not rely on and require any supervisory information.

To evaluate those commonly used algorithms, this chapter conducted experiments on open datasets. Criteria including root-mean-squared errors and coefficients of determination were adopted. Numerical results were also displayed in the experimental section for reference.

In more recent years, surveys showed that a deep learning model "Generative Adversarial Network (GAN)" has attracted much attention, and several novel imputation methods based on GANs have been proposed, e.g., MisGAN [49], MIWAE [50], and GAIN [51]. For future studies, deep learning architectures such as Deep PCA, PCANet, and Deep NMF, can be integrated into those four types of commonly used algorithms, namely, *K*-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches and subsequently enhance data imputation.
