• 目的： 简而言之，无监督学习识别无标签、无类别数据的共性（commonalities）。
• 用途：概率密度估计，即无监督推理数据的一个先验分布。
• 理解：无监督学习是为了学习而学习的范式。【Unsupervised learning is a paradigm designed to create autonomous intelligence by rewarding agents (that is, computer programs) for learning about the data they observe without a particular task in mind. In other words, the agent learns for the sake of learning。】
  A key motivation for unsupervised learning is that, while the data passed to learning algorithms is extremely rich in internal structure (e.g., images, videos and text), the targets and rewards used for training are typically very sparse (e.g., the label ‘dog’ referring to that particularly protean species, or a single one or zero to denote success or failure in a game).
  无监督算法的设计是为了理解数据本身。【This suggests that the bulk of what is learned by an algorithm must consist of understanding the data itself, rather than applying that understanding to particular tasks.】

常用方法类别：

• Clustering
  • hierarchical clustering,^[3]
  • k-means
  • mixture models
  • DBSCAN
  • OPTICS algorithm
• Anomaly detection
  • Local Outlier Factor
• Neural Networks
  • Autoencoders
  • Deep Belief Nets
  • Hebbian Learning
  • Generative Adversarial Networks
  • Self-organizing map
• Approaches for learning latent variable models such as
  • Expectation–maximization algorithm (EM)
  • Method of moments
  • Blind signal separation techniques
    • Principal component analysis
    • Independent component analysis
    • Non-negative matrix factorization
    • Singular value decomposition