aiqbench analysis
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.
What is the goal of unsupervised machine learning?
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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Full text under the publisher's open licence · 182 words
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