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.

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

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