We often come across problems where the dimensions of the data are huge. We might need to reduce the dimensions of the data in such a way that the reduced dimensional data best represents the original data. Principal Component Analysis (PCA) and autoencoders are some of the popular techniques to achieve this.

Although the intention of both these algorithms is the same for dimensionality reduction, there are some key differences in these two techniques:

• Unlike PCAs, autoencoders can learn non-linear feature representations from the data, which leads to enhanced model performance.
• PCAs are easier to train and more interpretable than autoencoders. The underlying math in autoencoders is also complex.
• PCA takes less time to run as compared to the autoencoders, which require more computation.