Dimensionality reduction: Difference between revisions
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== Methods == | == Methods == | ||
Some common methods to perform dimensionality reduction are listed as follows< | Some common methods to perform dimensionality reduction are listed as follows<ref name="flair">[https://data-flair.training/blogs/dimensionality-reduction-tutorial/ What is Dimensionality Reduction – Techniques, Methods, Components]</ref>: | ||
* Missing values: | * Missing values: | ||
Revision as of 18:23, 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[4]:
- Missing values:
- Low variance:
- Decision trees:
- Random forest:
- High correlation:
- Backward feature elimination:
- Factor analysis:
- Principal component analysis (PCA):