Dimensionality reduction in machine learning examples

1. What is dimensionality reduction explain with example?
2. Which of the following is an example of dimensionality reduction technique?
3. Where is dimensionality reduction used in machine learning?
4. What are dimensionality reduction techniques in machine learning?

What is dimensionality reduction explain with example?

Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.

Which of the following is an example of dimensionality reduction technique?

Principal Component Analysis (PCA), Factor Analysis (FA), Linear Discriminant Analysis (LDA) and Truncated Singular Value Decomposition (SVD) are examples of linear dimensionality reduction methods.

Where is dimensionality reduction used in machine learning?

Dimensionality reduction is commonly used in data visualization to understand and interpret the data, and in machine learning or deep learning techniques to simplify the task at hand.

What are dimensionality reduction techniques in machine learning?

Dimensionality reduction is a machine learning (ML) or statistical technique of reducing the amount of random variables in a problem by obtaining a set of principal variables.