Dimensionality reduction: Difference between revisions
No edit summary |
No edit summary |
||
| Line 19: | Line 19: | ||
* [[Kernel principal component analysis]] (Kernel PCA) | * [[Kernel principal component analysis]] (Kernel PCA) | ||
* [[Locally-Linear Embedding]] | * [[Locally-Linear Embedding]] | ||
== Methods == | |||
Some common methods to perform dimensionality reduction are listed as follows<refname="">[https://data-flair.training/blogs/dimensionality-reduction-tutorial/ 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): | |||
Revision as of 18:13, 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