Dimensionality reduction
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]
Components
Dimensionality reduction can be devided into two components or subcategories[3]:
- Feature selection: Consists in finding a subset of the original set of variables, and a subset aimed at modeling the problem. It usually involves three ways[4]:
- Wrappers
- Filters
- Embedded
- Feature extraction: Used to reduce the data in a high dimensional space to a lower dimension space[4].
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):