Unsupervised learning: Difference between revisions

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{{Machine learning|Paradigms}}
 
'''Unsupervised learning''' is a framework in [[machine learning]] where, in contrast to [[supervised learning]], algorithms learn patterns exclusively from unlabeled data.<ref name="WeiWu">{{Cite web |last=Wu |first=Wei |title=Unsupervised Learning |url=https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |access-date=26 April 2024 |archive-date=14 April 2024 |archive-url=https://web.archive.org/web/20240414213810/https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |url-status=live }}</ref> Other frameworks in the spectrum of supervisions include [[Weak supervision|weak- or semi-supervision]], where a small portion of the data is tagged, and [[Self-supervised learning|self-supervision]]. Some researchers consider self-supervised learning a form of unsupervised learning.<ref>{{Cite journal |last1=Liu |first1=Xiao |last2=Zhang |first2=Fanjin |last3=Hou |first3=Zhenyu |last4=Mian |first4=Li |last5=Wang |first5=Zhaoyu |last6=Zhang |first6=Jing |last7=Tang |first7=Jie |date=2021 |title=Self-supervised Learning: Generative or Contrastive |url=https://ieeexplore.ieee.org/document/9462394 |journal=IEEE Transactions on Knowledge and Data Engineering |pages=1 |doi=10.1109/TKDE.2021.3090866 |issn=1041-4347|arxiv=2006.08218 }}</ref>
'''Unsupervised learning''' is a framework in [[machine learning]] where,
 
 
 
 
 
contrast to [[supervised learning]], algorithms learn patterns exclusively from unlabeled data.<ref name="WeiWu">{{Cite web |last=Wu |first=Wei |title=Unsupervised Learning |url=https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |access-date=26 April 2024 |archive-date=14 April 2024 |archive-url=https://web.archive.org/web/20240414213810/https://na.uni-tuebingen.de/ex/ml_seminar_ss2022/Unsupervised_Learning%20Final.pdf |url-status=live }}</ref> Other frameworks in the spectrum of supervisions include [[Weak supervision|weak- or semi-supervision]], where a small portion of the data is tagged, and [[Self-supervised learning|self-supervision]]. Some researchers consider self-supervised learning a form of unsupervised learning.<ref>{{Cite journal |last1=Liu |first1=Xiao |last2=Zhang |first2=Fanjin |last3=Hou |first3=Zhenyu |last4=Mian |first4=Li |last5=Wang |first5=Zhaoyu |last6=Zhang |first6=Jing |last7=Tang |first7=Jie |date=2021 |title=Self-supervised Learning: Generative or Contrastive |url=https://ieeexplore.ieee.org/document/9462394 |journal=IEEE Transactions on Knowledge and Data Engineering |pages=1 |doi=10.1109/TKDE.2021.3090866 |issn=1041-4347|arxiv=2006.08218 }}</ref>
 
Conceptually, unsupervised learning divides into the aspects of data, training, algorithm, and downstream applications. Typically, the dataset is harvested cheaply "in the wild", such as massive [[text corpus]] obtained by [[Web crawler|web crawling]], with only minor filtering (such as [[Common Crawl]]). This compares favorably to supervised learning, where the dataset (such as the [[ImageNet|ImageNet1000]]) is typically constructed manually, which is much more expensive.
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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 ===