What is the goal of unsupervised machine learning?

2026-10-08

AI Summary

This study addresses the theoretical challenges arising from the ill-defined objectives of unsupervised learning and domain heterogeneity. Moving beyond the limitations of conventional unified definitions, this work systematically deconstructs and reconstructs the unsupervised learning paradigm through machine learning-theoretic analysis and conceptual taxonomy. It demonstrates that unsupervised learning does not constitute a singular objective but rather encompasses four core tasks: distribution estimation, data generation, feature extraction, and data understanding. To our knowledge, this is the first systematic delineation and elucidation of these four independent yet complementary objective dimensions. The establishment of this multi-dimensional objective framework provides clear theoretical guidance for subsequent algorithm design and evaluation in unsupervised 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.

Problem

• unsupervised learning
• machine learning
• goal identification
• distribution estimation
• feature extraction

Innovation

• Unsupervised Learning
• Distribution Estimation
• Data Generation
• Feature Extraction
• Data Understanding