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> | ||
Dimensionaliyy reduction can be devided into two subcategories<ref name="cognitive class">{{cite web |title=Machine Learning - Dimensionality Reduction - Feature Extraction & Selection |url=https://www.youtube.com/watch?v=AU_hBML2H1c |website=youtube.com |accessdate=24 March 2020}}</ref>: | |||
* Feature selection: | |||
** Wrappers | |||
** Filters | |||
** Embedded | |||
* Feature extraction: | |||
** Principal component analysis | |||
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"/>: | ||
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* [[Kernel principal component analysis]] (Kernel PCA) | * [[Kernel principal component analysis]] (Kernel PCA) | ||
* [[Locally-Linear Embedding]] | * [[Locally-Linear Embedding]] | ||
* [https://www.youtube.com/watch?v=3uxOyk-SczU] | |||
* [https://www.youtube.com/watch?v=AU_hBML2H1c] | |||
== References == | == References == | ||
Revision as of 00:51, 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]
Dimensionaliyy reduction can be devided into two subcategories[2]:
- Feature selection:
- Wrappers
- Filters
- Embedded
- Feature extraction:
- Principal component analysis
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