Dimensionality reduction in machine learning pdf

1. What is dimensionality reduction in machine learning?
2. What are 3 ways of reducing dimensionality?
3. Why dimensionality reduction is useful in machine learning?
4. Where is dimensionality reduction used in machine learning?

What is dimensionality reduction 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.

What are 3 ways of reducing dimensionality?

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

Why dimensionality reduction is useful in machine learning?

Dimensionality reduction brings many advantages to your machine learning data, including: Fewer features mean less complexity. You will need less storage space because you have fewer data. Fewer features require less computation time.

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.