Unsupervised learning: Difference between revisions

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== Probabilistic methods ==

Two of the main methods used in unsupervised learning are [[Principal component analysis|principal component]] and [[cluster analysis]]. [[Cluster analysis]] is used in unsupervised learning to group, or segment, datasets with shared attributes in order to extrapolate algorithmic relationships.<ref name="tds-ul" /> Cluster analysis is a branch of [[machine learning]] that groups the data that has not been [[Labeled data|labelled]], classified or categorized. Instead of responding to feedback, cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group.

A central application of unsupervised learning is in the field of [[density estimation]] in [[statistics]],<ref name="JordanBishop2004" /> though unsupervised learning encompasses many other domains involving summarizing and explaining data features. It can be contrasted with supervised learning by saying that whereas supervised learning intends to infer a [[conditional probability distribution]] conditioned on the label of input data; unsupervised learning intends to infer an [[a priori probability]] distribution .

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* [[Data clustering|Clustering]] methods include: [[hierarchical clustering]],<ref name="Hastie" /> [[k-means]],<ref name="tds-kmeans" /> [[mixture models]], [[model-based clustering]], [[DBSCAN]], and [[OPTICS algorithm]]

* [[Anomaly detection]] methods include: [[Local Outlier Factor]], and [[Isolation Forest]]

* Approaches for learning [[latent variable model]]s such as [[Expectation–maximization algorithm]] (EM), [[Method of moments (statistics)|Method of moments]], and [[Blind signal separation]] techniques ([[Principal component analysis]], [[Independent component analysis]], [[Non-negative matrix factorization]], [[Singular value decomposition]])

=== Method of moments ===