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{{Machine learning|Paradigms}}
'''Unsupervised learning''' is a framework in [[machine learning]] where,
'''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>▼
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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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