Unsupervised Learning Algorithm

A Unsupervised Learning Algorithm is a learning algorithm that can be implemented into an unsupervised learning system (to solve an unsupervised learning task).

• Example(s):
  • a Clustering Algorithm.
  • …
• Counter-Example(s):
  • Supervised Learning Algorithm, such as a semi-supervised learning algorithm.
• See: Unlabeled Data, Autoencoder.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Unsupervised_learning#Approaches Retrieved:2019-12-4.
  • Some of the most common algorithms used in unsupervised learning include:
    • Clustering.
      • hierarchical clustering, ** 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

1973

• (Duda & Hart, 1973) ⇒ Richard O. Duda, and Peter E. Hart. (1973). “Pattern Classification and Scene Analysis." John Wiley & Sons. ISBN:0471223611