Unsupervised learning: Difference between revisions

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Revision as of 23:22, 25 May 2024

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{{Short description|A paradigm in machine learning}}

'''Unsupervised learning''' is a method 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> Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself. No prior human intervention is needed.<ref name="WeiWu" />

Other methods in the supervision spectrum are [[Reinforcement Learning]] where the machine is given only a numerical performance score as guidance,<ref>{{Cite web |last=Ghahramani |first=Zoubin |title=Unsupervised learning |url=https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf |access-date=26 April 2024 |archive-date=12 November 2023 |archive-url=https://web.archive.org/web/20231112093614/https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf |url-status=live }}</ref> and [[Weak_supervision | Weak or Semi supervision]] where a small portion of the data is tagged, and [[Self-supervised_learning | Self Supervision]].

== Neural networks ==

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{{Reflist|

refs=

<ref name="tds-ul" >{{Cite web|url=https://towardsdatascience.com/unsupervised-machine-learning-clustering-analysis-d40f2b34ae7e|title=Unsupervised Machine Learning: Clustering Analysis|last=Roman|first=Victor|date=2019-04-21|website=Medium|access-date=2019-10-01|archive-date=2020-08-21|archive-url=https://web.archive.org/web/20200821132257/https://towardsdatascience.com/unsupervised-machine-learning-clustering-analysis-d40f2b34ae7e|url-status=live}}</ref>

<ref name="JordanBishop2004">{{cite book |first1=Michael I. |last1=Jordan |first2=Christopher M. |last2=Bishop |chapter=7. Intelligent Systems §Neural Networks |editor-first=Allen B. |editor-last=Tucker |title=Computer Science Handbook |url=https://www.taylorfrancis.com/books/mono/10.1201/9780203494455/computer-science-handbook-allen-tucker |edition=2nd |publisher=Chapman & Hall/CRC Press |year=2004 |doi=10.1201/9780203494455 |isbn=1-58488-360-X |access-date=2022-11-03 |archive-date=2022-11-03 |archive-url=https://web.archive.org/web/20221103234201/https://www.taylorfrancis.com/books/mono/10.1201/9780203494455/computer-science-handbook-allen-tucker |url-status=live }}</ref>

<ref name="Hastie" >{{harvnb|Hastie|Tibshirani|Friedman|2009|pp=485–586}}</ref>

<ref name="tds-kmeans" >{{Cite web|url=https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1|title=Understanding K-means Clustering in Machine Learning|last=Garbade|first=Dr Michael J.|date=2018-09-12|website=Medium|language=en|access-date=2019-10-31|archive-date=2019-05-28|archive-url=https://web.archive.org/web/20190528183913/https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1|url-status=live}}</ref>

<ref name="TensorLVMs" >{{cite journal |last1=Anandkumar |first1=Animashree |last2=Ge |first2=Rong |last3=Hsu |first3=Daniel |last4=Kakade |first4=Sham |first5= Matus |last5=Telgarsky |date=2014 |title=Tensor Decompositions for Learning Latent Variable Models |url=http://www.jmlr.org/papers/volume15/anandkumar14b/anandkumar14b.pdf |journal=Journal of Machine Learning Research |volume=15 |pages=2773–2832 |bibcode=2012arXiv1210.7559A |arxiv=1210.7559 |access-date=2015-04-10 |archive-date=2015-03-20 |archive-url=https://web.archive.org/web/20150320201108/http://jmlr.org/papers/volume15/anandkumar14b/anandkumar14b.pdf |url-status=live }}</ref>

<ref name="Buhmann" >{{Cite book|last1=Buhmann|first1=J.|last2=Kuhnel|first2=H.|title= &#91;Proceedings 1992&#93; IJCNN International Joint Conference on Neural Networks|volume=4|pages=796–801|publisher=IEEE|doi=10.1109/ijcnn.1992.227220|isbn=0780305590|chapter=Unsupervised and supervised data clustering with competitive neural networks|year=1992|s2cid=62651220}}</ref>

<ref name="Comesana" >{{Cite journal|last1=Comesaña-Campos|first1=Alberto|last2=Bouza-Rodríguez|first2=José Benito|date=June 2016|title=An application of Hebbian learning in the design process decision-making|journal=Journal of Intelligent Manufacturing|volume=27|issue=3|pages=487–506|doi=10.1007/s10845-014-0881-z|s2cid=207171436|issn=0956-5515}}</ref>

<ref name="Carpenter" >{{cite journal|author1=Carpenter, G.A.|author2=Grossberg, S.|name-list-style=amp|year=1988|title=The ART of adaptive pattern recognition by a self-organizing neural network|journal=Computer|volume=21|issue=3|pages=77–88|url=http://www.cns.bu.edu/Profiles/Grossberg/CarGro1988Computer.pdf|doi=10.1109/2.33|s2cid=14625094|access-date=2013-09-16|archive-date=2018-05-16|archive-url=https://web.archive.org/web/20180516131553/http://www.cns.bu.edu/Profiles/Grossberg/CarGro1988Computer.pdf|url-status=dead}}</ref>

<ref name="Hinton2010" >{{cite book | last = Hinton |first=G. | date=2012 |chapter = A Practical Guide to Training Restricted Boltzmann Machines |chapter-url=http://www.cs.utoronto.ca/~hinton/absps/guideTR.pdf |publisher=Springer |title=Neural Networks: Tricks of the Trade |series=Lecture Notes in Computer Science |volume=7700 |pages=599–619 |doi=10.1007/978-3-642-35289-8_32 |isbn=978-3-642-35289-8 |access-date=2022-11-03 |archive-date=2022-09-03 |archive-url=https://web.archive.org/web/20220903215809/http://www.cs.utoronto.ca/~hinton/absps/guideTR.pdf |url-status=live }}</ref>

<ref name="HintonMlss2009" >{{cite web |people=Hinton, Geoffrey |date=September 2009 |title=Deep Belief Nets |type=video |url=https://videolectures.net/mlss09uk_hinton_dbn |access-date=2022-03-27 |archive-date=2022-03-08 |archive-url=https://web.archive.org/web/20220308022539/http://videolectures.net/mlss09uk_hinton_dbn/ |url-status=live }}</ref>

}}

== Further reading ==

{{refbegin}}

* {{cite book |editor1=Bousquet, O. |editor3=Raetsch, G. |editor2=von Luxburg, U. |editor2-link= Ulrike von Luxburg |title=Advanced Lectures on Machine Learning |url=https://archive.org/details/springer_10.1007-b100712 |publisher=Springer |year=2004 |isbn=978-3540231226 }}

* {{cite book |author1=Duda, Richard O. |author2-link=Peter E. Hart |author2=Hart, Peter E. |author3=Stork, David G. |year=2001 |chapter=Unsupervised Learning and Clustering |title=Pattern classification |edition=2nd |publisher=Wiley |isbn=0-471-05669-3|author1-link=Richard O. Duda |title-link=Pattern classification }}

*{{cite book |first1=Trevor |last1=Hastie |authorlink1=Trevor Hastie |first2=Robert |last2=Tibshirani |authorlink2=Robert Tibshirani |first3=Jerome |last3=Friedman |chapter=Unsupervised Learning |chapter-url=https://link.springer.com/chapter/10.1007/978-0-387-84858-7_14 |title=The Elements of Statistical Learning: Data mining, Inference, and Prediction |year=2009 |publisher=Springer |isbn=978-0-387-84857-0 |pages=485–586 |doi=10.1007/978-0-387-84858-7_14 |access-date=2022-11-03 |archive-date=2022-11-03 |archive-url=https://web.archive.org/web/20221103234204/https://link.springer.com/chapter/10.1007/978-0-387-84858-7_14 |url-status=live }}

* {{cite book |editor1-last=Hinton |editor1-first=Geoffrey |editor-link=Geoffrey Hinton |editor2-last=Sejnowski |editor2-first=Terrence J. |editor2-link=Terrence J. Sejnowski |year=1999 |title=Unsupervised Learning: Foundations of Neural Computation |publisher=[[MIT Press]] |isbn=0-262-58168-X}}

{{refend}}