{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/shell-theory-a-statistical-model-of-reality","title":"Shell Theory: A Statistical Model of Reality","arxiv_id":null,"date":"2021-05-28","proceeding":"IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 5","authors":["Wen-Yan Lin","Siying Liu","Changhao Ren","Ngai-Man Cheung","Hongdong Li","Yasuyuki Matsushita"],"abstract":"The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is\r\nrooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process.","url_abs":"https://ieeexplore.ieee.org/document/9444188","url_pdf":"http://www.kind-of-works.com/papers/shell_theory_preprint.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"shell-theory-a-statistical-model-of-reality","repo_url":"https://github.com/wen-yan-lin/shell-theory","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"one-class-classifier","task_name":"One-class classifier"},{"task_slug":"unsupervised-anomaly-detection-with-specified-6","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-5","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-7","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-4","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-assira-cat-vs-dog","task":"Anomaly Detection","dataset":"ASSIRA Cat Vs 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