## 4 Conclusions  
This paper proposes a framework that has the potential to improve the compactness and separability properties of data points, which consistently boosts the performance of any off-the-shelf classifier, or a semi-supervised clustering model. We have utilized the generative capacity of autoencoders to reconstruct data points, thus preserving the original input feature space. This characteristic of our proposed framework makes it easily pluggable before a classifier model (not limited by a specific classifier type) or semi-supervised clustering model. We have also utilized an array of MLP regressors for mapping the latent representation of the input samples to approximations of the centroids of the classes. We conducted experiments on five different datasets (MNIST, Fashion-MNIST, USPS, MSTAR, and WDBC) to demonstrate the results of our proposed method. We concluded that plugging our model as a preprocessing step gives better results in all the datasets in both classification and semi-supervised clustering tasks. We also compared the proposed method to other existing methods on enhancing the classification accuracy over the WDBC and MSTAR datasets. We illustrated the importance of augmenting small datasets with the produced approximation of the centroids of the classes and how it further improves the classifier results.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12031713/s1](https://www.mdpi.com/article/10.3390/app12031713/s1)]([https://www.mdpi.com/article/10.3390/app12031713/s1](https://www.mdpi.com/article/10.3390/app12031713/s1)).  
Conceptualization, M.M.E. and R.F.; methodology, M.M.E. and R.F.; software, M.M.E.; validation, M.M.E., R.F. and Y.E.-S.; formal analysis, R.F. and Y.E.-S.; investigation, M.M.E.; resources, M.M.E. and R.F.; data curation, M.M.E.; writing--original draft preparation, M.M.E.; writing--review and editing, R.F. and Y.E.-S.; visualization, M.M.E.; supervision, R.F and Y.E.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available online. MNIST: [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 10 December 2021); Fashion-MNIST: [[https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion](https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion)]([https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion](https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion)) (accessed on 10 December 2021); USPS: [[https://www.kaggle.com/bistatumanga/usps-dataset](https://www.kaggle.com/bistatumanga/usps-dataset)]([https://www.kaggle.com/bistatumanga/usps-dataset](https://www.kaggle.com/bistatumanga/usps-dataset)) (accessed on 10 December 2021); MSTAR: [[https://www.sdms.afrl.afl.infl/index.php?collection=mstar&page=targets](https://www.sdms.afrl.afl.infl/index.php?collection=mstar&page=targets)]([https://www.sdms.afrl.afl.infl/index.php?collection=mstar&page=targets](https://www.sdms.afrl.afl.infl/index.php?collection=mstar&page=targets)) (accessed on 10 December 2021); WDBC: [[https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+)]([https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+))(Diagnostic) (accessed on 10 December 2021).  
The authors would like to thank all anonymous reviewers and editors for their helpful suggestions for the improvement of this paper.  
Figure 6: TSNE visualizations of the test set without mapping (left) vs. mapped with our model (right): (**a**) MNIST; (**b**) Fashion-MNIST; (**c**) USPS; (**d**) MSTAR; (**e**) WDBC.