[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122931-en":3,"doc-seo-122931-105":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},122931,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","ANALYTiC - Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning - Dissertation","Compact handheld and sensor devices produce abundant spatio-temporal trajectory data from animals, humans, and vehicles, enabling discovery of patterns that can support more efficient interpretation. ANALYTiC is proposed using active learning to infer semantic annotations from trajectories by leveraging sets of labeled samples. The study combines dimensionality reduction with decision boundary analysis to reveal clusters and structural separations in trajectory datasets. Experiments on three trajectory datasets demonstrate improved interpretability and enhanced efficiency and accuracy in trajectory labeling, supporting broader integration of machine learning and visual analysis for movement data.","ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine  \nLearning  \nHaidri, Salman  \nDepartment of Computer Science Memorial University of Newfoundland and Labrador  \nSupervised by Dr. Amilcar Soares  \nA dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science (Honours) in Computer Science.  \nDecember 2023  \nAbstract  \nThe advent of compact, handheld devices has given us a pool of tracked movement data that could be used to infer trends and patterns that can be made to use. With this flooding of various trajectory data of animals, humans, vehicles, etc., the idea of ANALYTiC originated, using active learning to infer semantic annotations from the trajectories by learning from sets of labeled data. This study explores the application of dimensionality reduction and decision boundaries in combination with the already present active learning, highlighting patterns and clusters in data. We test these features with three different trajectory datasets with objective of exploiting the the already labeled data and enhance their interpretability. Our experimental analysis exemplifies the potential of these combined methodologies in improving the efficiency and accuracy of trajectory labeling. This study serves as a stepping-stone towards the broader integration of machine learning and visual methods in context of movement data analysis.  \nTo Hamdan  \niv  \nAcknowledgements  \nI would like to extend my deepest gratitude towards Dr. Amilcar Soares, without whose invaluable guidance and support this paper was not possible. His mentorship has been instrumental, and I sincerely appreciate the opportunities he’s given me to explore, grow, and learn.  \nA huge shout-out to my friends, especially Yaksh for being an amazing friend and supporting me constantly throughout this journey.  \nAnd to my family, your support means everything to me.  \nTable of contents  \nTitle page i  \nAbstract iii  \nAcknowledgements v  \nTable ofcontents vi  \nList of figures viii  \n1 Introduction 1  \n2 Background 4  \n2.1 Dimensionality Reduction Techniques.............................................................. 4  \n2.2 Decision Boundaries ........................................................................................... 6  \n2.2.1 Types of Decision Boundaries .............................................................. 6  \n2.2.2 Visualization of Decision Boundaries ................................................... 6  \n2.2.3 Significance of Decision Boundaries.......................................................7  \n2.2.4 Practical Implications and Applications ...............................................7  \n2.3 Trajectory Concepts and Terminologies.......................................................... 8  \n2.4 Active Learning Strategies................................................................................. 8  \n3 Experimenting with Trajectory Data 10  \n3.1 Methodology for dimensionality reduction......................................................12  \n3.2 Methodology for decision surface .....................................................................14  \n4 Results and Discussions 17  \n4.1 Plots from dimensionality reduction................................................................ 17  \n4.2 Plots from decision surface ...............................................................................19  \n5 The ANALYTiC tool 23  \n6 Conclusion 30  \nBibliography 31  \nList of figures  \n2.1 Dimensionality reduction workflow................................................................... 4  \n2.2 Decision surface workflow .................................................................................. 8  \n3.1 Code snippet illustrating the dimensionality reduction workflow ................ 11  \n3.2 Code snippet illustrating the dimensionality reduction workflow ................ 11  \n3.3 Code snippet illustrating the decisi","cbCaika7ymeTiDHX","https://ap.wps.com/l/cbCaika7ymeTiDHX","pdf",1581140,1,43,"English","en",105,"# Introduction\n## Background\n## Experimenting with Trajectory Data\n## Results and Discussions\n## The ANALYTiC tool\n## Conclusion","[{\"question\":\"What is the core goal of ANALYTiC?\",\"answer\":\"ANALYTiC aims to infer semantic annotations from trajectory data by combining active learning with methods that improve interpretability through clusters and data structure analysis.\"},{\"question\":\"How do dimensionality reduction and decision boundaries work together in the study?\",\"answer\":\"Dimensionality reduction is used to expose underlying structure in the trajectory data, while decision boundary analysis highlights how classes separate, supporting better pattern understanding and labeling efficiency.\"},{\"question\":\"What datasets are used to evaluate the proposed approach?\",\"answer\":\"The evaluation uses three different trajectory datasets, including examples shown in the figure list such as Geo life, fishing vessel, and Starkey animal data.\"}]","ANALYTiC - Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning - Dissertation | PDF",1785813751,108,{"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},"analytic-understanding-decision-boundaries-and-dimensionality-reduction-in-machine-learning-dissertation","",{"@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/analytic-understanding-decision-boundaries-and-dimensionality-reduction-in-machine-learning-dissertation/122931/",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","2026-08-04",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 is the core goal of ANALYTiC?","Question",{"text":75,"@type":76},"ANALYTiC aims to infer semantic annotations from trajectory data by combining active learning with methods that improve interpretability through clusters and data structure analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do dimensionality reduction and decision boundaries work together in the study?",{"text":80,"@type":76},"Dimensionality reduction is used to expose underlying structure in the trajectory data, while decision boundary analysis highlights how classes separate, supporting better pattern understanding and labeling efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"What datasets are used to evaluate the proposed approach?",{"text":84,"@type":76},"The evaluation uses three different trajectory datasets, including examples shown in the figure list such as Geo life, fishing vessel, and Starkey animal data.","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"]