Applications of Autoencoders

How Autoencoders Outperform PCA in Dimensionality Reduction

Dimensionality reduction with autoencoders using non-linear data

Photo by Shubham Dhage on Unsplash

There are so many practical applications of autoencoders. Dimensionality reduction is one of them.

There are so many techniques for dimensionality reduction. Autoencoders (AEs) and Principal Component Analysis (PCA) are popular among them.

PCA is not suitable for dimensionality reduction in non-linear data. In contrast, autoencoders work really well with non-linear data in dimensionality reduction.

Objectives

At the end of this article, you’ll be able to

• Use Autoencoders to reduce the dimensionality of the input data
• Use PCA to reduce the dimensionality of the input data
• Compare the performance of PCA and Autoencoders in dimensionality reduction
• See how Autoencoders outperform PCA in dimensionality reduction
• Learn key differences between PCA and Autoencoders
• Learn when to use which method for dimensionality reduction

Prerequisites