Unsupervised learning

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In machine learning, unsupervised learning is a class of problems in which one seeks to determine how the data are organized. It is distinguished from supervised learning (and reinforcement learning) in that the learner is given only unlabeled examples.

Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data.

One form of unsupervised learning is clustering. Another example is blind source separation based on Independent Component Analysis (ICA).

Among neural network models, the Self-organizing map (SOM) and Adaptive resonance theory (ART) are commonly used unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg(1988).

Bibliography

• Sergios Theodoridis, Konstantinos Koutroumbas: "Pattern Recognition", 4th Edition, Academic Press, 2009.
• Geoffrey Hinton, Terrence J. Sejnowski (editors) (1999): Unsupervised Learning: Foundations of Neural Computation, MIT Press, ISBN 0-262-58168-X (This book focuses on unsupervised learning in neural networks.)
• S. Kotsiantis, P. Pintelas: Recent Advances in Clustering: A Brief Survey, WSEAS Transactions on Information Science and Applications, Vol 1, No 1 (73-81), 2004.
• Richard O. Duda, Peter E. Hart, David G. Stork: Unsupervised Learning and Clustering, Ch. 10 in Pattern classification (2nd edition), p. 571, Wiley, New York, ISBN 0-471-05669-3, 2001.
• Ranjan Acharyya (2008): A New Approach for Blind Source Separation of Convolutive Sources, ISBN-10: 3639077970

ISBN-13: 978-3639077971(this book focuses on unsupervised learning with Blind Source Separation)

See also

• Multivariate analysis
• Artificial neural network
• Data clustering
• Expectation-maximization algorithm
• Self-organizing map
• Radial basis function network
• Generative topographic map
• Blind Source Separation