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Learning from the Shape of Data

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

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Abstract

To make sense of large data sets, we often look for patterns in how data points are ``shaped” in the space of possible measurement outcomes. The emerging field of topological data analysis (TDA) offers a toolkit for formalizing the process of identifying such shapes. This paper aims to discover why and how the resulting analysis should be understood as reflecting significant features of the systems that generated the data. I argue that a particular feature of TDA---its functoriality---is what enables TDA to translate visual intuitions about structure in data into precise, computationally tractable descriptions of real-world systems.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Rosenstock, Saritasarita.rosenstock@anu.edu.au0000-0001-9715-0010
Additional Information: Forthcoming in Philosophy of Science, December 2021
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: 14 Apr 2021 02:39
Last Modified: 14 Apr 2021 02:39
Item ID: 18910
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-dev.library.pitt.edu/id/eprint/18910

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