Dimensionality reduction: Difference between revisions
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Dimensionality reduction is one of the main applications of [[unsupervised learning]] . It can be understood as the process of reducing the number of random variables under consideration by getting a set of principal variables.<ref name="pythonistaplanet.com">{{cite web |title=Real World Applications of Unsupervised Learning |url=https://pythonistaplanet.com/applications-of-unsupervised-learning/ |website=pythonistaplanet.com |accessdate=23 March 2020}}</ref> | '''Dimensionality reduction''' is one of the main applications of [[unsupervised learning]] . It can be understood as the process of reducing the number of random variables under consideration by getting a set of principal variables.<ref name="pythonistaplanet.com">{{cite web |title=Real World Applications of Unsupervised Learning |url=https://pythonistaplanet.com/applications-of-unsupervised-learning/ |website=pythonistaplanet.com |accessdate=23 March 2020}}</ref> | ||
Some of the most common dimensionality reduction algorithms in machine learning are listed as follows<ref name="pythonistaplanet.com"/>: | Some of the most common dimensionality reduction algorithms in machine learning are listed as follows<ref name="pythonistaplanet.com"/>: | ||
* [[Principal Component Analysis]] (PCA) | * [[Principal Component Analysis]] (PCA) | ||
* [[Kernel]] PCA | * [[Kernel principal component analysis]] (Kernel PCA) | ||
* [[Locally-Linear Embedding]] | * [[Locally-Linear Embedding]] | ||
== References == | == References == | ||
Revision as of 00:34, 24 March 2020
Dimensionality reduction is one of the main applications of unsupervised learning . It can be understood as the process of reducing the number of random variables under consideration by getting a set of principal variables.[1]
Some of the most common dimensionality reduction algorithms in machine learning are listed as follows[1]:
- Principal Component Analysis (PCA)
- Kernel principal component analysis (Kernel PCA)
- Locally-Linear Embedding