Unsupervised learning

Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

Unsupervised learning algorithms

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

Read more on Wikipedia.

« Back to Glossary Index