Unsupervised Machine Learning System

An Unsupervised Machine Learning System is a machine learning system that can infer the structure of the input signal or feedback from unlabeled data.

• Context
  • It implements an Unsupervised Machine Learning Algorithm to solve an Unsupervised Machine Learning Task.
• Example(s):
  • a Clustering System.
  • an Association Rule Learning System.
  • an Unsupervised Artificial Neural Network such as:
    • Self-Organizing Map.
    • Hebbian Learning System.
    • Generative Adversarial Network.
  • an Unsupervised Feature Learning System.
  • …
• Counter-Example(s)
  • a Supervised Learning System.
  • a Reinforcement Learning System.
  • a Semi-supervised Learning System.
• See: Learning System, Active Learning System, Principal component analysis, Independent Component Analysis, Non-negative Matrix factorization, Singular Value Decomposition.

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References

2017A

• (Sammut & Webb, 2017) ⇒ "Unsupervised Learning". In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1304-1304
  • QUOTE: Unsupervised learning refers to any machine learning process that seeks to learn structure in the absence of either an identified output (cf. supervised learning) or feedback (cf. reinforcement learning). Three typical examples of unsupervised learning are clustering, association rules, and self-organizing maps.

2017B

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Unsupervised_learning Retrieved:2017-12-24.
  • Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations). Since the examples given to the learner are unlabeled, there is no evaluation of the accuracy of the structure that is output by the relevant algorithm — which is one way of distinguishing unsupervised learning from supervised learning and reinforcement learning.

    A central case of unsupervised learning is the problem of density estimation in statistics, though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining key features of the data.

    Approaches to unsupervised learning include:

    • Clustering.
      • k-means.
      • mixture models.
      • hierarchical clustering,
    • Anomaly detection
    • Neural Networks
      • Hebbian Learning
      • Generative Adversarial Networks.
    • Approaches for learning latent variable models such as
      • Expectation–maximization algorithm (EM)
      • Method of moments.
      • Blind signal separation techniques, e.g.,
        • Principal component analysis,
        • Independent component analysis,
        • Non-negative matrix factorization,
        • Singular value decomposition.

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