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This paper clarifies the fragmented objectives of unsupervised learning, distinguishing it from supervised and reinforcement learning by identifying four distinct goals: distribution estimation, data generation, feature extraction, and data understanding. By rejecting a single universal definition for this field, the work provides necessary conceptual clarity for researchers navigating the diverse landscape of representation learning and generative modeling.
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What is the goal of unsupervised machine learning?

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arXiv:2610.11697v1 Announce Type: new Abstract: Unsupervised learning is one of the main branches of machine learning. Here I argue that unlike the other branches of machine learning (supervised and reinforcement learning), unsupervised learning is a rather heterogenous field that can serve several different goals. It seems futile to try to define one single goal for unsupervised learning. I identify four different goals for unsupervised learning: 1) Estimating the distribution, 2) Generating new data points, 3) Extracting features for downstream tasks, and 4) Understanding the data.

Title:What is the goal of unsupervised machine learning?

Abstract:Unsupervised learning is one of the main branches of machine learning. Here I argue that unlike the other branches of machine learning (supervised and reinforcement learning), unsupervised learning is a rather heterogenous field that can serve several different goals. It seems futile to try to define one single goal for unsupervised learning. I identify four different goals for unsupervised learning: 1) Estimating the distribution, 2) Generating new data points, 3) Extracting features for downstream tasks, and 4) Understanding the data.

Machine Learning (cs.LG)

arXiv:2610.11697 [cs.LG]

(or arXiv:2610.11697v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.11697 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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