Dimensionality reduction: Difference between revisions
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Dimensionaliy 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: | * Feature selection: | ||
Revision as of 00:54, 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]
Categories
Dimensionaliy reduction can be devided into two subcategories[2]:
- Feature selection:
- Wrappers
- Filters
- Embedded
- Feature extraction:
Algorithms
Some of the most common dimensionality reduction algorithms in machine learning are listed as follows[1]:
- Principal Component Analysis
- Kernel principal component analysis (Kernel PCA)
- Locally-Linear Embedding