[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120436-en":3,"doc-seo-120436-105":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},120436,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Robust Machine Learning for Complex Data - Enhancing Efficiency and Powering Healthcare Solutions - Thesis","This thesis addresses the challenge of extracting reliable insights from large, heterogeneous and noise-prone datasets where classical machine learning assumptions often fail. It targets robustness issues such as sensitivity to outliers and perturbations arising from Euclidean distance dependence. Contributions include adaptive objectives for dimensionality reduction, a robust graphical representation method, and structure-aware representation learning. It further develops efficient optimization via PALM, GIRM, and Riemannian optimization with T-RGD, validated through medical and bioinformatics applications.","ROBUST MACHINE LEARNING FOR COMPLEX DATA: ENHANCING EFFICIENCY AND  \nPOWERING HEALTHCARE SOLUTIONS  \nby  \nXiangyu Li  \n© Copyright by Xiangyu Li, 2024  \nAll Rights Reserved  \nA thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science) .  \nGolden, Colorado  \nDate    \nSigned:   Xiangyu Li  \nSigned:    \nDr. Hua Wang Thesis Advisor  \nGolden, Colorado  \nDate    \nSigned:    \nDr. Iris Bahar  \nProfessor and Department Head Department of Computer Science  \nABSTRACT  \nIn today’s data-driven world, the demand for extracting meaningful insights from vast datasets is increasing alongside the growing complexity of machine learning models. Traditional machine learning faces constraints, including development in noiseless or low-noise environments and heavy reliance on assumptions that may not hold in real-world scenarios. A notable limitation is their dependence on Euclidean distances, making them vulnerable to perturbations introduced by outliers or inherent noise in real-world datasets. To address these challenges, this research focuses on designing machine learning algorithms that exhibit robustness and adaptability to large-scale, heterogeneous data. Key contributions include adaptive objectives for dimensionality reduction, a novel method for robust graphical representation, and structure-awareness in representation learning. These advancements bridge the gap between complex data structures and actionable insights in both medical and computational domains.  \nEffective optimization is crucial in data-driven research, especially as many machine learning methods suffer from non-convex or non-smooth optimization problems. My research explores the Proximal Alternating Linearized Minimization (PALM) technique for robust convergence and superior prediction accuracy. Additionally, the Generalized Iteratively Reweighted Method (GIRM) is found to effectively manage non-convex objectives. Furthermore, my research introduces Riemannian Optimization, which transforms simplex problems into a smooth manifold, and proposes a Tangent Riemannian Gradient Descent (T-RGD) method that improves the efficiency and convergence in optimization. These contributions provide robust and efficient tools for reliable analysis across diverse real-world datasets.  \nAs the complexity and volume of medical data increase, advanced AI-driven methodologies become necessary. This research leverages our proposed machine learning methods for enriched representation learning in longitudinal chest X-ray analysis, enabling early diagnosis. Additionally, our research delves into bioinformatics, uncovering protein interactions and exploring drug repurposing for COVID-19 . These machine learning-driven approaches contribute to improved healthcare outcomes and deeper scientific insights.  \nIn conclusion, this thesis advances machine learning’s theoretical foundations and practical applications, providing robust models, streamlined optimization methods, and AI-powered healthcare solutions. It addresses real-world complexities for improved healthcare and scientific insights.  \nTABLE OF CONTENTS  \nABSTRACT ................................................... iii  \nLIST OF FIGURES .............................................. viii  \nLIST OF TABLES ................................................ xi  \nACKNOWLEDGMENTS ........................................... xiii  \nCHAPTER 1 INTRODUCTION ........................................ 1  \n1.1 Motivation ............................................... 1  \n1.2 Outline ................................................. 2  \n1.2.1 Machine Learning Framework: Robustness, Adaptability, and Scalability ......... 2  \n1.2.2 Theoretical Foundations Driving Efficient Optimization Techniques ............ 3  \n1.2.3 AI-Powered Healthcare: Advancing Medical and Bioinformatics Domains ........ 4  \n1.2.4 Summary and Future Work ...","cbCairRQPCTey4IL","https://ap.wps.com/l/cbCairRQPCTey4IL","pdf",13297680,1,121,"English","en",105,"# Abstract\n# Chapter 1 Introduction\n## Motivation\n## Outline\n# Chapter 2 On Adaptive Principal Component Analysis and Its Implementations\n## Adaptive loss function\n## Alternating linearized minimization\n## The Solution Algorithm\n## Experiments\n# Chapter 3 On Mean-Optimal Robust Linear Discriminant Analysis\n## Formulation and Algorithm\n## Experiment\n# Chapter 4 Beyond the Simplex: Hadamard-Infused Deep Sparse Representations for Enhanced Similarity Measures\n## Abstract","[{\"question\":\"What limitations of traditional machine learning motivate this research?\",\"answer\":\"The thesis highlights constraints in noisy or low-noise settings and heavy reliance on assumptions that may not hold in real-world scenarios. It also points out sensitivity to perturbations caused by outliers or inherent noise due to dependence on Euclidean distances.\"},{\"question\":\"What are the main research contributions beyond standard machine learning models?\",\"answer\":\"The work proposes adaptive objectives for dimensionality reduction, a novel robust graphical representation method, and structure-aware representation learning. These aim to connect complex data structures to actionable insights in medical and computational domains.\"},{\"question\":\"How does the thesis improve optimization efficiency and convergence?\",\"answer\":\"It studies Proximal Alternating Linearized Minimization (PALM) for robust convergence and improved prediction accuracy, and uses the Generalized Iteratively Reweighted Method (GIRM) to handle non-convex objectives. It also introduces Riemannian Optimization and a Tangent Riemannian Gradient Descent (T-RGD) method to enhance efficiency for simplex-related problems.\"}]","Robust Machine Learning for Complex Data - Enhancing Efficiency and Powering Healthcare Solutions - Thesis | PDF",1785730101,305,{"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},"robust-machine-learning-for-complex-data-enhancing-efficiency-and-powering-healthcare-solutions-thesis","",{"@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/robust-machine-learning-for-complex-data-enhancing-efficiency-and-powering-healthcare-solutions-thesis/120436/",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","2026-08-03",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},"What limitations of traditional machine learning motivate this research?","Question",{"text":75,"@type":76},"The thesis highlights constraints in noisy or low-noise settings and heavy reliance on assumptions that may not hold in real-world scenarios. It also points out sensitivity to perturbations caused by outliers or inherent noise due to dependence on Euclidean distances.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main research contributions beyond standard machine learning models?",{"text":80,"@type":76},"The work proposes adaptive objectives for dimensionality reduction, a novel robust graphical representation method, and structure-aware representation learning. These aim to connect complex data structures to actionable insights in medical and computational domains.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis improve optimization efficiency and convergence?",{"text":84,"@type":76},"It studies Proximal Alternating Linearized Minimization (PALM) for robust convergence and improved prediction accuracy, and uses the Generalized Iteratively Reweighted Method (GIRM) to handle non-convex objectives. 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