Shell Theory: A Statistical Model of Reality

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Shell Theory: A Statistical Model of Reality
Title:
Shell Theory: A Statistical Model of Reality
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
28 May 2021
Citation:
Lin, W.-Y., Liu, S., Ren, C., Cheung, N.-M., Li, H., & Matsushita, Y. (2021). Shell Theory: A Statistical Model of Reality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. doi:10.1109/tpami.2021.3084598
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 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.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : SC20/19-128610-CORE

This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 1
Grant Reference no. : N.A
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0162-8828
1939-3539
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