Dimensionality reduction: Difference between revisions
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* [https://www.youtube.com/watch?v=AU_hBML2H1c] | * [https://www.youtube.com/watch?v=AU_hBML2H1c] | ||
* [http://courses.washington.edu/css581/lecture_slides/17_dimensionality_reduction.pdf] | * [http://courses.washington.edu/css581/lecture_slides/17_dimensionality_reduction.pdf] | ||
* [https://www.coursera.org/lecture/big-data-machine-learning/dimensionality-reduction-PDGeA] | |||
== References == | == References == | ||
Revision as of 18:20, 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] High dimensionality has many costs, including redundant and irrelevant features which degrade the performance of some algorithms, difficulty in interpretation and visualization, and infeasible computation.[2]
Categories
Dimensionality reduction can be devided into two subcategories[3]:
- 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
Methods
Some common methods to perform dimensionality reduction are listed as follows<refname="">What is Dimensionality Reduction – Techniques, Methods, Components</ref>:
- Missing values:
- Low variance:
- Decision trees:
- Random forest:
- High correlation:
- Backward feature elimination:
- Factor analysis:
- Principal component analysis (PCA):
References
- ↑ 1.0 1.1 Real World Applications of Unsupervised Learning
- ↑ Dimensionality Reduction courses.washington.edu
- ↑ Template:Cite web