无监督学习没有单一目标：一篇论文拆出四种不同目标
What is the goal of unsupervised machine learning?
这篇论文把无监督学习拆成四个目标，分别对应密度估计、生成、特征提取和数据分析，做研究或选方法时能帮你对号入座。
arXiv 论文 2610.11697 指出，无监督学习与监督学习、强化学习不同，是一个异质领域，试图给它定义单一目标是徒劳的。作者识别出四个具体目标：估计数据分布、生成新数据点、为下游任务提取特征、以及理解数据本身。这一划分有助于理清不同无监督方法各自在优化什么。
What is the goal of unsupervised machine learning?
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.