A Comprehensive Guide to Dimensionality Reduction

An exhaustive compilation of dimensionality reduction techniques.

You are performing predictive analytics with 100s of features, and your model is taking an exceptionally long time to train, and the results are still not satisfactory!!!

Looks like an issue we all face at one point or another, so what approach should we take to handle the problem?

The first step is to explore the input features to check if you need all of the input features or if you need to reduce the number of input features and only utilize the most important or relevant features to the problem space.

Dimensionality reduction techniques address the problems with high-dimensional data by reducing the number of input features and only maintaining the relevant attributes for prediction.

Are you ready to explore the topic of dimensionality reduction?

This post will include

• What is dimensionality reduction?
• Why do we need dimensionality reduction?
• Different techniques of dimensionality reduction

1. Feature selection using Filter and Wrapper method
2. Feature extraction for linear data using Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA)
3. Feature extraction for non-linear data using Kernel…