Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
The paper presents the LS-PIE framework aimed at enhancing the interpretability of linear latent variable models (LLVMs) such as PCA and ICA. The framework introduces techniques for latent ranking, scaling, clustering, and condensing, enhancing the informativeness and usability of latent space representations. Here are some suggestions.
1. The paper could benefit from a more comprehensive literature review, particularly on recent advancements in latent variable models and their applications.
2. An analysis of the computational complexity and scalability of the proposed methods would be beneficial, especially for large-scale datasets and high-dimensional data.
3. The numerical investigation provides a clear demonstration of the framework's capabilities. However, including a comparison with baseline methods would provide a more robust validation.
4. The impact section convincingly argues the practical benefits of the framework. The conclusion summarizes the contributions effectively but could include more specific details on potential future enhancements.
Comments on the Quality of English Language
The quality of English is good.
Author Response
Thank you for taking the time to review our paper, please see the attachment for our responses and paper updates.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis paper proposes a Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) framework to enhance latent space representations and improve the interpretability of linear latent spaces. Specifically, the framework enhances the information representation and interpretability of latent variables from four aspects: latent ranking (LR), latent scaling (LS), latent clustering (LC), and latent condensing (LCON).
Overall, the paper has the following issues that need to be addressed for further improvement:
1. Could you design a rigorous usage logic for LR, LS, LC, and LCON to enhance the generalizability of the framework?
2. The experimental section could benefit from incorporating multi-channel data to increase the rigor of the experiments.
3. It is recommended to apply this framework to real-world data to enhance the persuasiveness of the experiments. Currently, the experimental design is too thin to directly prove the effectiveness of the extracted latent variables.
4. The discussion section of the experiments is too simplistic. It is suggested to further elaborate on it to highlight the contributions of this work.
Comments on the Quality of English LanguageThe quality of English language in this paper is generally good.
Author Response
Thank you for taking the time to review our paper, please see the attachment for our responses and paper updates.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsI would like to congratulate the authors for this article. I am a researcher who works a lot in this area and it is a permanent challenge to be able to interpret the latent variables in a data analysis. For this reason, I greatly appreciate the effort and time dedicated to this manuscript. I have very much enjoyed reviewing this paper.
I see that the authors have used Python for the implementation of the algorithms, and that's fine, I'm not criticizing it, but I think it's worth working on an implementation in R (which can be shared through a package) that would allow many other data analysts to have access to the proposed framework.
I think the background needs to be improved. There are missing equations, and some details about the models on which this research is based are missing. I leave this to the authors' discretion, but the background of the current version is poor.
Have the authors considered in their work PCA variants such as Sparse PCA or Disjoint PCA? Similarly, there are variants in the ICA, CCA and FA models that I consider should be discussed (there are even important papers that should be cited). I would like to see some explanation of this in the paper by adding a few lines.
I see a huge potential in this research. Have the authors considered extending this work to tensors of order 3 (or 3-way tables) using for example models like PARAFAC or Tucker3? Explain and comment with a few lines in the manuscript (at least worth mentioning as future work).
This is not mandatory: I would like the authors to seriously consider carrying out more computational experiments. Adding at least one more example with real data. Additionally, conducting a full simulation study. I think a proposal like the one the authors are making deserves it.
I do not like the way the authors have worded the conclusions. I recommend that this section, which is fundamental, be worded better.
Authors might even consider creating a table summarizing the most important findings/results/benefits of their research.
I believe that this is a complete work but that it can be improved if the authors consider my suggestions.
Comments on the Quality of English LanguageWhen reading this manuscript I noticed that some parts of the writing are not in good English. I recommend that authors carry out a proofreading.
Author Response
Thank you for taking the time to review our work, please see the attached document for detailed responses.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsNo more issues.
Author Response
Thank you so much for taking your time, and providing your valuable insights and critiques of our work.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe manuscript has been revised satisfactorily. I have no further comments or suggestions.
Author Response
Thank you so much for taking your time, and providing your valuable insights and critiques of our work.
Reviewer 3 Report
Comments and Suggestions for AuthorsMy comments have been adequately responded to by the authors of the manuscript. This work has been significantly improved. I recommend that this work be published.
