Finding True Clusters: On the Importance of Simplicity in Science

Erkenntnis 87 (5):2081-2096 (2020)
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Abstract

The main point of this paper is to underscore the link between simplicity and truth in an unsupervised machine learning context. More precisely, we argue that parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such notions of simplicity is truth conducive. The truth that we are looking for are specific geometrical shapes and we know which algorithm can find which shapes provided that we pay attention to parametric and dimensional simplicity.

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References found in this work

Prediction versus accommodation and the risk of overfitting.Christopher Hitchcock & Elliott Sober - 2004 - British Journal for the Philosophy of Science 55 (1):1-34.
Instrumentalism, Parsimony, and the Akaike Framework.Elliott Sober - 2002 - Philosophy of Science 69 (S3):S112-S123.
Instrumentalism, parsimony, and the akaike framework.Elliott Sober - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S112-S123.
Simplicity and model selection.Guillaume Rochefort-Maranda - 2016 - European Journal for Philosophy of Science 6 (2):261-279.

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