# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 5 Discussion  
In this paper, we introduce a novel deep multi-view subspace clustering approach based on reconstructing self-expressive matrix (DCRSM) that goes beyond the conventional self-expressive model. In the proposed DCRSM, we build a reconstruction module to approximate the self-expressive matrix for each view of the dataset. By leveraging this capability, our approach offers improved flexibility and efficiency in the training process, making it suitable for real-world applications. Meanwhile, our approach takes the intrinsic relationship between samples into account and utilizes shared and specific layers to integrate common and specific representations of each sample. By combining both common and specific features explicitly, our approach facilitates the comprehensive utilization of information across multiple views. Experiment results on publicly available datasets have shown the superiority of our method. In our study, we mainly concentrate on the scalability and accuracy of multi-view clustering. The main limitation of our proposed network is two-stage training, which adds complexity to the training process. In future work, we consider extending our model to an end-to-end model, which can simplify the training process.  
Conceptualization, Z.S. and H.Z.; methodology, Z.S. and H.Z.; software, Z.S.; validation, Z.S.; formal analysis, Z.S. and H.Z.; investigation, Z.S. and H.Z.; resources, Z.S. and H.Z.; data curation, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, Z.S. and H.Z.; visualization, Z.S.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 62173143 and Grant 61973122.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)) Fashion-MNIST accessed on 15 June 2023, [[https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php](https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php)]([https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php](https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php)) COIL-20 accessed on 15 June 2023, [[https://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php](https://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php)]([https://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php](https://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php)) COIL-100 accessed on 15 June 2023, and [[http://www.cs.tau.ac.il/~wolf/ytfaces/index.html](http://www.cs.tau.ac.il/~wolf/ytfaces/index.html)]([http://www.cs.tau.ac.il/~wolf/ytfaces/index.html](http://www.cs.tau.ac.il/~wolf/ytfaces/index.html)) YTF accessed on 15 June 2023.  
The authors declare no conflict of interest.  
Figure 7: The t-SNE visualization of our proposed method on all experimental datasets.