Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model
Round 1
Reviewer 1 Report
The authors have made a great job in the paper. Excellent and clear breakdown of the background information of the methodology, and interesting experimental results.
Some minor comments:
- I suggest adding the number of datasets experimented with as part of the Abstract or the names of the datasets.
- "To be specific, the traditional
clustering methods include SSC [6], EDSC [24], LRSC [27].
The relatively superior HSI clustering methods are S4C [15],
EGCSC [31] and EKGCSC [31]."
I suggest adding the full name of the methods as those are the first mention of them.
Author Response
- I suggest adding the number of datasets experimented with as part of the Abstract or the names of the datasets.
Experiment results showed that the SSAKGCSC model can achieve improved segmentation performance and better noise resistance ability.
Revised: Experiment results on Salinas, Indian Pines, Pavia Center, and Pavia University data sets showed that the SSAKGCSC model can achieve improved segmentation performance and better noise resistance ability.
- "To be specific, the traditional clustering methods include SSC[6],EDSC[24],LRSC[27]. The relatively superior HSI clustering methods are S4C[15] EGCSC[31]and EKGCSC[31]."I suggest adding the full name of the methods as those are the first mention of them
Revised:
"To be specific, the traditional clustering methods include Sparse Subspace Clustering (SSC)[6], Efficient Dense Subspace Clustering (EDSC)[24], Low-rank Subspace Clustering (LRSC)[27]. The relatively superior HSI clustering methods are Spectral-Spatial Subspace Clustering (S4C)[15] Efficient Graph Convolutional Subspace Clustering (EGCSC)[31]and Efficient Kernel Graph Convolutional Subspace Clustering (EKGCSC)[31]."
Reviewer 2 Report
The authors of the paper describe their proposed approach for "Robust Spectral Clustering Incorporating Statistical Sub-graph Affinity Model". The topic is interesting and with possible applicability. However, the paper needs several improvements:
1) the main contribution and originality should be explained in more detail, is the new clustering method?
2) the motivation of the approach needs further clarification, why this work is important?
3) discussion of related work in clustering should be expanded with more recent work, in particular, please consider fuzzy clustering as related work
4) Minor grammar and syntax issues need correction to enhance readability
5) more simulation results and formal comparison of results are needed
6) the conclusions should be extended with more future work
7) More references to recent clustering papers could be included
8) The manuscript is not in the format of Axioms paper
9) Please define all variables and parameters in the equations
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 3 Report
Thank you for inviting me as a reviewer for a manuscript titled Robust Spectral Clustering Incorporating Statistical Sub-graph Affinity Model. The paper is impressive for the efforts made by you to demonstrate the valence of your model. The model is well explained and the methodology is clear. But, the paper would be more exciting if you implement the below improvements:
. Need to better highlight the novelty of the study in the introduction. Why is the topic important (or why do you study it)? What are the research questions? What are your contributions? Why is it to propose this particular method (IPFCM))? What is the power of the proposed algorithms that you are exploring in this research?
. Better define the motivations.
. Literature review. The clustering techniques are widely used for engineering optimisations, in AI etc. Authors should provide more recent relevant references (from 2019-2022). Remove papers published before 2019.
. Generally, validation and comparisons of the results is well prepared.
. The conclusion section - The authors will have to demonstrate the impact and insights of the research. Add limitations of the model.
Scientific soundness :
. The subject addressed in this paper is relevant.
Interest to the readers :
. In my opinion, method of this paper seem to be interesting for the readership of the journal.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
The authors have addressed all my concerns and the paper could be accepted.
