Understanding Dimensionality Reduction: A 10-Minute Guide

Josephreeves
4 min readMar 8, 2024

In the era of big data, datasets with numerous features are ubiquitous. While this abundance of data is valuable, it often poses challenges in analysis and modeling due to the curse of dimensionality. Dimensionality reduction techniques offer a solution by condensing high-dimensional data into a more manageable form while retaining essential information. In this guide, we’ll explore the concepts, benefits, and common techniques of dimensionality reduction.

What is Dimensionality Reduction?

Dimensionality reduction is the process of reducing the number of features in a dataset while preserving its important structure and relationships. By eliminating redundant or irrelevant features, dimensionality reduction aims to simplify data analysis, improve computational efficiency, and enhance the performance of machine learning algorithms.

Why is Dimensionality Reduction Important?

  1. Curse of Dimensionality: As the number of features increases, the amount of data required to effectively cover the feature space grows exponentially. This can lead to sparsity in the data, increased computational complexity, and overfitting in machine learning models. Dimensionality reduction helps mitigate these issues by reducing the number of…

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Josephreeves

A data enthusiast, learning and helping others learn about data. Follow me for more data related content