Pca Vs Autoencoders For A Small Dataset In Dimensionality Reduction

A recent study has found that general machine learning algorithms can outperform neural networks when trained on small datasets. This is in contrast to the usual belief that neural networks require large datasets to achieve good results. Autoencoders, a type of neural network, are used in a variety of applications such as image denoising, image generation, image colorization, image compression, and image super-resolution. A comparison of autoencoders and PCA trained on the MNIST dataset showed that the autoencoder model outperformed the PCA model.