Rosenstock, Sarita (2020) Learning from the Shape of Data. In: UNSPECIFIED.

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Abstract

This paper examines the epistemic value of using topological methods to study the "shape" of data sets. It is argued that the category theoretic notion of "functoriality" aids in translating visual intuitions about structure in data into precise, computable descriptions of real-world systems.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Rosenstock, Sarita sarita.rosenstock@anu.edu.au 0000-0001-9715-0010
Additional Information:	Conditionally accepted to the contributed papers volume of the 2020 PSA. This is an early draft and comments are welcome.
Keywords:	data science, representation, models
Subjects:	General Issues > Data Specific Sciences > Mathematics > Practice Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Depositing User:	Sarita Rosenstock
Date Deposited:	29 Jul 2020 04:13
Last Modified:	29 Jul 2020 04:13
Item ID:	17680
Subjects:	General Issues > Data Specific Sciences > Mathematics > Practice Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Date:	2020
URI:	https://philsci-archive.pitt.edu/id/eprint/17680

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• Learning from the Shape of Data. (deposited 29 Jul 2020 04:13) [Currently Displayed]
  • Learning from the Shape of Data. (deposited 14 Apr 2021 02:39)

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