[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118652-en":3,"doc-seo-118652-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118652,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Refining Optimization Methods for Training Machine Learning Models - A Case Study in Robotic Surgical Procedures","Machine learning enables predictive modeling by learning patterns from data so results generalize to unseen cases, but every learning task depends on optimization challenges. Conventional training optimizers are often inadequate for different application requirements, motivating refinements across learning components. This thesis develops core optimization methods for machine learning by improving optimizers and model compression strategies to enhance resilience and effectiveness. It proposes scalable low-rank matrix factorization for Gaussian Process Regression using convex-nonconvex formulations with incremental updates, and introduces an adaptive stochastic gradient descent method based on a non-uniform p-norm for convex and nonconvex settings, including remote surgical gesture detection.","Refining Optimization Methods for Training Machine Learning Models: A Case Study in Robotic Surgical  \nProcedures  \nFrancis Boabang  \nA Thesis  \nin  \nConcordia Institute for Information Systems Engineering  \nPresented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy(Information Systems Engineering)  \nat  \nConcordia University  \nMontral, Qubec, Canada  \nDecember 2024  \n© Francis Boabang, 2024  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Francis Boabang  \nEntitled: Refining Optimization Methods for Training Machine Learning Models: A Case  \nStudy in Robotic Surgical Procedures  \nand submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy(Information Systems Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Marius Paraschivoiu  \n  External Examiner Dr. Christian Gagn  \n Dr. Juergen Rilling  Arms-Length Examiner  \n  Examiner  \nDr. Yang Wang  \n  Examiner  \nDr. Jamal Bentahar  \n Dr. Farnoosh Naderkhani  Thesis Supervisor  \nApproved by  Dr. Farnoosh Naderkhani, Graduate Program Director  Concordia Insitute for Information Systems Engineering  \nDate of Defence: September 4, 2024  Dr. Mourad Debbabi, Dean   \nGina Cody School of Engineering and Computer Science  \nAbstract  \nRefining Optimization Methods for Training Machine Learning Models: A Case Study in Robotic  \nSurgical Procedures  \nFrancis Boabang, Ph.D.  \nConcordia University, 2024  \nMachine learning is a technology that builds predictive models from data, allowing generalization to unseen cases. At the core of every learning problem lies an optimization challenge, and solving these problems reliably is crucial to resolving the obstacles surrounding machine learning. Primarily, conventional optimization algorithms employed for training machine learning frequently are often ill-suited for various applications. Concerted efforts are needed to refine and optimize various components of machine learning training. This thesis explores fundamental optimization algorithms across various machine learning applications. By enhancing optimization schemes, including optimizers and model compression techniques, the resilience and effectiveness of machine learning applications can be improved.  \nThe first segment of the thesis introduces an innovative low-rank matrix factorization scheme aimed at enhancing the scalability of machine learning. Gaussian Process Regression is used as the machine learning model to scale with low rank matrix factorization in this section of the thesis due to its lightweight nature, which enables the incremental updating of model parameters online prior to prediction. A nonconvex formulation of a low-rank matrix factorization (SRLSMF) with convex formulation of a low-rank matrix factorization initialization (ℓ1-SRLSMF), is advocated to scale Gaussian Process Regression (GPR) . Thus, by employing convex nonconvex low rank matrix factorization to scale a given the Gaussian Process Regression model, the model can avoid local minima and converge to a solution with smaller recovery residuals. Also, the running time of convex nonconvex low rank matrix factorization is expected to be smaller than that of applying nonconvex low rank matrix factorization alone under the same stopping criterion. To the best of our knowledge, the machine learning method proposed in this thesis is the first to exploit nonconvex formulation of a low-rank matrix factorization (SRLSMF) with convex formulation of a low-rank matrix factorization initialization (ℓ1-SRLSMF) to scale machine learning in machine learning domain. Recognizing the cost-prohibitive nature of standard eigen decomposition for online Gaussian Process Regression covariance update, we implement incremental eigen decomposition within the ℓ1-SRLSMF and SRLSMF ","cbCaih0rF7CWsizv","https://ap.wps.com/l/cbCaih0rF7CWsizv","pdf",1464053,1,123,"English","en",105,"# Abstract\n## Low-rank matrix factorization for scalable GPR\n## Incremental eigen decomposition for online covariance updates\n## Applications to robotic suturing tasks\n## Adaptive stochastic gradient descent using non-uniform p-norm\n## Theoretical guarantees and gesture detection\n## Future research directions","[{\"question\":\"Why is optimization important for training machine learning models?\",\"answer\":\"Every learning problem includes an optimization challenge, and reliable optimization is crucial for overcoming obstacles and improving training outcomes.\"},{\"question\":\"What is the main idea of the proposed scalable low-rank matrix factorization for Gaussian Process Regression?\",\"answer\":\"The thesis advocates a convex-nonconvex low-rank matrix factorization formulation and uses incremental updates to scale GPR, helping avoid local minima and reduce recovery residuals.\"},{\"question\":\"How does the adaptive stochastic gradient descent (ASGD) method work in this thesis?\",\"answer\":\"ASGD leverages a non-uniform p-norm concept by assigning different coordinate categories with distinct base learning rates, and it provides theoretical guarantees for convex and nonconvex settings, including detection of suturing gestures.\"}]","Refining Optimization Methods for Training Machine Learning Models - A Case Study in Robotic Surgical Procedures | PDF",1785684723,310,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"refining-optimization-methods-for-training-machine-learning-models-a-case-study-in-robotic-surgical-procedures","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/refining-optimization-methods-for-training-machine-learning-models-a-case-study-in-robotic-surgical-procedures/118652/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is optimization important for training machine learning models?","Question",{"text":75,"@type":76},"Every learning problem includes an optimization challenge, and reliable optimization is crucial for overcoming obstacles and improving training outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main idea of the proposed scalable low-rank matrix factorization for Gaussian Process Regression?",{"text":80,"@type":76},"The thesis advocates a convex-nonconvex low-rank matrix factorization formulation and uses incremental updates to scale GPR, helping avoid local minima and reduce recovery residuals.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the adaptive stochastic gradient descent (ASGD) method work in this thesis?",{"text":84,"@type":76},"ASGD leverages a non-uniform p-norm concept by assigning different coordinate categories with distinct base learning rates, and it provides theoretical guarantees for convex and nonconvex settings, including detection of suturing gestures.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]