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Data Classification Algorithms and Tverberg-type theorems De Loera, Jesus
Description
The classical Tverberg's theorem says that a set with sufficiently many points in $R^d$ can always be partitioned into m parts so that the (m - 1)-simplex is the (nerve) intersection pattern of the convex hulls of the parts. Motivated by questions from the performance of data classification algorithms such as multi-class logistic regression method, we investigated other version Tverberg. Our main results demonstrate that Tverberg's theorem is but a special case of a much more general situation. Given sufficiently many points, any tree or cycle, can also be induced by at least one partition of the point set. The proofs require a deep investigation of oriented matroids and order types. We also present new results on the distribution of simplicial complexes arising from the classification of data. (Joint work with Deborah Oliveros, Tommy Hogan, Dominic Yang (supported by NSF).)
Item Metadata
Title |
Data Classification Algorithms and Tverberg-type theorems
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2019-10-07T09:44
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Description |
The classical Tverberg's theorem says that a set with sufficiently many points in $R^d$ can always be partitioned into m parts so that the (m - 1)-simplex is the (nerve) intersection pattern of the convex hulls of the parts. Motivated by questions from the performance of data
classification algorithms such as multi-class logistic regression method, we investigated other version Tverberg. Our main results demonstrate that Tverberg's theorem is but a special case of a much more general situation. Given sufficiently many points, any tree or cycle, can also be induced by at least one partition of the point set. The proofs require a deep investigation of oriented matroids and order types.
We also present new results on the distribution of simplicial complexes arising from the classification of data.
(Joint work with Deborah Oliveros, Tommy Hogan, Dominic Yang (supported by NSF).)
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Extent |
40.0 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: University of California
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Series | |
Date Available |
2020-04-05
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0389731
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International