Difference between revisions of "Unsupervised Learning Algorithm"

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== References ==
 
== References ==
  
=== 2013 ===
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* (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Unsupervised_learning
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=== 2019 ===
** Approaches to unsupervised learning include:
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* (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Unsupervised_learning#Approaches Retrieved:2019-12-4.
*** [[data clustering|clustering]] (e.g., [[k-means]], [[mixture models]], [[hierarchical clustering]]),
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** Some of the most common algorithms used in unsupervised learning include:
*** [[hidden Markov model]]s,
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*** [[Data clustering|Clustering]]
*** [[blind signal separation]] using [[feature extraction technique]]s for [[dimensionality reduction]] (e.g., [[principal component analysis]], [[independent component analysis]], [[non-negative matrix factorization]], [[singular value decomposition]]).<ref>[[Ranjan Acharyya|Acharyya, Ranjan]] (2008); ''A New Approach for Blind Source Separation of Convolutive Sources'', ISBN 978-3-639-07797-1 (this book focuses on unsupervised learning with Blind Source Separation) </ref>
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****[[hierarchical clustering]], ** [[k-means]]  
** Among [[neural network]] models, the [[self-organizing map]] (SOM) and [[adaptive resonance theory]] (ART) are commonly used unsupervised learning [[algorithm]]s. 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).<ref>{{cite journal|author=Carpenter, G.A. and Grossberg, S.|year=1988|title=The ART of adaptive pattern recognition by a self-organizing neural network|journal=Computer|volume=21|pages=77–88|url=http://www.cns.bu.edu/Profiles/Grossberg/CarGro1988Computer.pdf}}</ref>
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**** [[mixture models]]
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**** [[DBSCAN]]
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**** [[OPTICS algorithm]]
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*** [[Anomaly detection]]
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**** [[Local Outlier Factor]]
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*** [[Artificial neural network|Neural Networks]]
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****[[Autoencoder]]s
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****[[Deep belief network|Deep Belief Nets]]
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****[[Hebbian Learning]]
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****[[Generative adversarial network]]s
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****[[Self-organizing map]]
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*** Approaches for learning [[latent variable model]]s such as
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**** [[Expectation–maximization algorithm]] (EM)
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**** [[Method of moments (statistics)|Method of moments]]
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**** [[Blind signal separation]] techniques
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***** [[Principal component analysis]]
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***** [[Independent component analysis]]
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***** [[Non-negative matrix factorization]]
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***** [[Singular value decomposition]]
 
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Latest revision as of 19:01, 4 December 2019