## 5 Conclusions and Future Work  
In this paper, we introduced a new method called IGSAE that works with interactive guiding layers. It is a dimensionality reduction method that combines the ideas of feature selection and feature extraction. There are two key points. One is using guiding layers to flexibly update the weights. This offers the advantages of feature selection and manages the effects of each feature to reduce classification performance loss. The other is a constraint method by Wasserstein distance and KL divergence. Wasserstein distance prevents overfitting in guiding layers, and KL divergence restricts the useless nodes of the network. These two main ideas interactively perform to extract the main information of the origin feature and reduce the dimensionality. In the experiment, we compared IGSAE with other traditional dimensionality reduction methods and show better performance. The proposed method outperformed other methods on average and showed the highest classification accuracies because the coefficients of \(J_{guide}\) and \(J_{was}\) are efficiently optimized through the experimental tests.  
As a future work, we need to expand our methodology to vision tasks by applying convolutional layers to ensure compatibility for image data and high-dimensional data representation [50]. Dimensionality reduction methods are used not only for efficiency and performance but also for visualization to show a representative data structure.  
Conceptualization, S.K.; methodology, L.H.; software, H.L. and C.H.; validation, H.L. and C.H.; formal analysis, H.L. and B.I.; investigations, H.L.; resources, H.L.; data curation, H.L.; writing--original draft preparation, H.L. and S.K.; writing--review and editing, H.L., C.H., B.I. and S.K.; visualization, H.L. and B.I.; supervision, H.L. and C.H.; project administration, S.K.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by Inha University Grant.  
Not applicable.  
Every dataset can be found as follows: Heart ([[https://archive.ics.uci.edu/ml/datasets/heart+disease](https://archive.ics.uci.edu/ml/datasets/heart+disease)]([https://archive.ics.uci.edu/ml/datasets/heart+disease](https://archive.ics.uci.edu/ml/datasets/heart+disease))) accessed 11 June 2023, Wine ([[https://archive.ics.uci.edu./ml/datasets/](https://archive.ics.uci.edu./ml/datasets/)]([https://archive.ics.uci.edu./ml/datasets/](https://archive.ics.uci.edu./ml/datasets/)) wine+quality) accessed 11 June 2023, USPS ([[https://www.csie.ntu.edu.tw/~glin/libsvmtools/datasets/](https://www.csie.ntu.edu.tw/~glin/libsvmtools/datasets/)]([https://www.csie.ntu.edu.tw/~glin/libsvmtools/datasets/](https://www.csie.ntu.edu.tw/~glin/libsvmtools/datasets/)) multiclass.html#usps) accessed 11 June 2023, Fashion-MNIST ([[https://www.kaggle.com/datasets/zalando-research/fashionmnist](https://www.kaggle.com/datasets/zalando-research/fashionmnist)]([https://www.kaggle.com/datasets/zalando-research/fashionmnist](https://www.kaggle.com/datasets/zalando-research/fashionmnist))) accessed 11 June 2023, Yale ([[http://vision.ucsd.edu/~leekc/ExtYaleDatabase/](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/)]([http://vision.ucsd.edu/~leekc/ExtYaleDatabase/](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/)) Yale%20 Face%20 Database.htm) Accessed 22 December 2020, and COIL-100 ([[https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php](https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php)]([https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php](https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php))) accessed 11 June 2023.  
All authors declare that they have no conflict of interest.