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 PCA, Non-Negative Matrix Factorization(NMF), IsoMap, t-SNE, and UMAP
4. Autoencoders using deep neural network

What is dimensionality reduction, why do we need it, and how does it help build better models?

Dimensionality reduction is the process to transform high dimension data space to a lower dimension data space still maintaining the essence of the data

A higher dimension space creates a Curse of dimensionality, which challenges data analysis, visualization, and machine learning model training due to data sparsity and data concentration.

When a dataset has 100s of input features, additional training samples are required to capture all possible combinations for the model to generalize better, but it also leads to data sparsity where a lot of input features have…