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|a 10.1109/TPAMI.2021.3084598
|2 doi
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|a eng
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| 100 |
1 |
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|a Lin, Wen-Yan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Shell Theory
|b A Statistical Model of Reality
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Revised 15.09.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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 rooted 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
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|a Journal Article
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| 700 |
1 |
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|a Liu, Siying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ren, Changhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheung, Ngai-Man
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Hongdong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Matsushita, Yasuyuki
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 10 vom: 02. Okt., Seite 6438-6453
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:10
|g day:02
|g month:10
|g pages:6438-6453
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|u http://dx.doi.org/10.1109/TPAMI.2021.3084598
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