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.

Categories

Dimensionaliy reduction can be devided into two subcategories:

• Feature selection:
  • Wrappers
  • Filters
  • Embedded
• Feature extraction:
  • Principal component analysis

Algorithms

Some of the most common dimensionality reduction algorithms in machine learning are listed as follows:

• Principal Component Analysis
• Kernel principal component analysis (Kernel PCA)
• Locally-Linear Embedding

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