PCA vs Autoencoder
- Although PCA is fundamentally a linear transformation, auto-encoders may describe complicated non-linear processes.
- Because PCA features are projections onto the orthogonal basis, they are completely linearly uncorrelated. However, since autoencoded features are only trained for correct reconstruction, they may have correlations.
- PCA is quicker and less expensive to compute than autoencoders.
- PCA is quite similar to a single layered autoencoder with a linear activation function.
- Because of the large number of parameters, the autoencoder is prone to overfitting. (However, regularization and proper planning might help to prevent this).
How is Autoencoder different from PCA
In this article, we are going to see how is Autoencoder different from Principal Component Analysis (PCA).