Group-based local adaptive deep multiple kernel learning with lp norm

Shengbing Ren; Fa Liu; Weijia Zhou; Xian Feng; Chaudry Naeem Siddique

doi:10.1371/journal.pone.0238535

Peer Review History

Original SubmissionApril 1, 2020
6 Jul 2020 Decision Letter - Robertas Damasevicius, Editor PONE-D-20-09329 Group-based Local Adaptive Deep Multiple Kernel Learning with Lp Norm PLOS ONE Dear Dr. Ren, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Aug 20 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Robertas Damasevicius Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service. Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://learn.aje.com/plos/) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services. If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free. Upon resubmission, please provide the following: The name of the colleague or the details of the professional service that edited your manuscript A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a supporting information file) A clean copy of the edited manuscript (uploaded as the new manuscript file) 3. We note that Figures 6, 7, 8, 9, 10 in your submission contain copyrighted images. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright. We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission: 3.1. You may seek permission from the original copyright holder of Figures 6, 7, 8, 9, 10 to publish the content specifically under the CC BY 4.0 license. We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text: “I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.” Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission. In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].” 3.2. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only. 4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: THe authors used a group-based local adaptive approach to enhance the existing deep multiple kernel Learning (DMKL) method toenhance the classification accuracy . Four basic kernels , Arc-cosine kernel then small sample sizes and low dimensions UCI datasets and Caltech-256 dataset with large sample sizes and high dimensions have been utilized to evaluate the performance of the GLDMKL approach. The authors provide several analyses and comparisons therefore i recommend the manuscript to be published in the journal Reviewer #2: The authors proposed new algorithms called DMKL. In general the paper is well written but very poor formatted. Moreover, the following issues must be addressed: 1) The quality of images must be improved. 2) The quality of charts must be improved. 3) Used bibliography must be changed. Use mainly the papers from the last 3-4 years to show the current state of knowledge. 4) More explanation is needed for what exactly is the novelty of this paper -- it must be more underlined in the abstract and related works. 5) Backgound sections must be merged to related works. 6) Some theoretical analysis of the convergence of your solution is needed. 7) More experiments must be added. 8) More comparisons with the lastest solutions are needed. 9) More statistical analysis should also be added and discussed. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes:** dalia yousri Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0238535.r001
Revision 1
13 Aug 2020 Author Response Dear Reviewers， Thank you for your review of our manuscript (ID: PONE-D-20-09329), which is titled “Group-based local adaptive deep multiple kernel learning with lp norm”. We appreciate your concerns and suggestions, and have revised our manuscript accordingly. The followings are our responses for your comments. Point1: The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Response1: We supplement and refine the experimental data, add corresponding analysis and further explain the ambiguities. All the modifications are marked in Yellow in the paper. Point2: Has the statistical analysis been performed appropriately and rigorously? Response2: Thanks to the reviewers for affirming that there are detailed statistical analysis in my paper. In the experiment of UCI datasets, we have discussed comparison of classification performance, the effect of group numbers on classification performance and the effect of layer numbers on classification performance. In the experiment of Caltech-256 datasets, we randomly select five types of data with similar shapes: bowling-ball, car-tire, desk-globe, roulette-wheel and sunflower-101. When these five datasets are respectively used as positive, we have analyzed the classification accuracy under different numbers of layers and groups. Special thanks for your good suggestions. Point3: The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Response3: The data in my experiment has been provided as part of the paper. There are no restrictions on publicly sharing data. The url is: https://github.com/fage8/GLDMKL.git. Point4: PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Response4: The language usage, spelling and grammar have been completely revised in the paper. All the modifications are marked in Yellow in the paper. For example, lp norm is modified to the lp norm. classical DMKL is modified to the classical DMKL. Point5: The authors provide several analyses and comparisons therefore i recommend the manuscript to be published in the journal. Response5: Thank your recommendation about our paper. We make further modifications to our paper. We supplement and refine the experimental data, add corresponding analysis and further explain the ambiguities. Point6: The quality of images must be improved. Response6: Figures 1-5 and Figure 11 have been modified, and some key elements in the figures have been modified and added with color labels. Figures 6-10 have been removed from the paper. Point7: The quality of charts must be improved. Response7: All tables in the paper are changed to three-line tables, and their captions and header elements are marked in bold. Point8: Used bibliography must be changed. Use mainly the papers from the last 3-4 years to show the current state of knowledge. Response8: Thank you for your excellent suggestions. We add the papers from the last 3-4 years as references. All the modifications are marked in Yellow in the paper. Point9: More explanation is needed for what exactly is the novelty of this paper -- it must be more underlined in the abstract and related works. Response9: The novelties of this paper are underlined in the abstract and related works. The novelties of this paper are the followings: our GLDMKL method can divide samples into multiple groups according to the multiple kernel k-means clustering algorithm. The learning process in each well-grouped local space is exactly adaptive deep multiple kernel learning. And our structure is adaptive, so there is no fixed number of layers. The learning model in each group is trained independently, so the number of layers of the learning model maybe different which highlights the flexibility of the model. Our model is more adaptable to data of different dimensions and sizes. All the modifications are marked in Yellow in the paper. Point10: Backgound sections must be merged to related works. Response10: Special thanks for your good suggestions. Background section is merged to related works. Some descriptions about DMKL have been integrated and some new references have been added. Point11: Some theoretical analysis of the convergence of your solution is needed. Response11: We add some theoretical analysis of the convergence of our method. In our method, we transform the non-convex problem into a convex function problem through a gate function. In this way, the local minimum must be found by the gradient ascent method, so our method must converge. Point12: More experiments must be added. Response12: The latest solutions are added in our paper. The latest method DWS-MKL in 2020 is added to the classification accuracy comparison experiment. Specifically, these added experiments include the comparison experiment of best classification performance and the comparison experiment of classification performance at each layer. Point13: More comparisons with the lastest solutions are needed. Response13: The latest method DWS-MKL in 2020 is added to the classification accuracy comparison experiment. Specifically, these comparisons include the comparison of best classification performance and the comparison of classification performance at each layer. In comparison experiment, the classification performance of our GLDMKL method is better than DWS-MKL method on various datasets. Point14: More statistical analysis should also be added and discussed. Response14: We have discussed the effect of group numbers and the effect of layer numbers. Some statistical analysis will be added in futrue work in Validation section. For example, the probabilistic grouping can be added in our model, the influence of the lp norm can be discussed and the stability of model can be discussed. More reasons and modifications have been noted in the article, all the modifications are marked in Yellow in the paper. Special thanks to you for your good comments. Sincerely yours, Shengbing Ren, Fa Liu, Weijia Zhou, Xian Feng and Chaudry Naeem Siddique Attachments Attachment Submitted filename: renamed_f587d.docx https://doi.org/10.1371/journal.pone.0238535.r002
19 Aug 2020 Decision Letter - Robertas Damasevicius, Editor Group-based local adaptive deep multiple kernel learning with lp norm PONE-D-20-09329R1 Dear Dr. Ren, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Robertas Damasevicius Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: All the comments have been addressed I dont have another additional comments therefore the manuscript should be accepted in the journal. Reviewer #2: The paper can be accepted. All comments have been addressed and the quality of the paper is ok for PLOS ONE. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No https://doi.org/10.1371/journal.pone.0238535.r003
Formally Accepted
8 Sep 2020 Acceptance Letter - Robertas Damasevicius, Editor PONE-D-20-09329R1 Group-based local adaptive deep multiple kernel learning with lp norm Dear Dr. Ren: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Professor Robertas Damasevicius Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0238535.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .