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PCA vs Autoencoders for a Small Dataset in Dimensionality Reduction
Neural Networks and Deep Learning Course: Part 45
Can general machine learning algorithms outperform neural networks with small datasets?
In general, deep learning algorithms such as neural networks require a massive amount of data to achieve reasonable performance. So, neural networks like autoencoders can benefit from very large datasets that we use to train the models.
Sometimes, general machine learning algorithms can outperform neural network algorithms when they are trained with very small datasets.
Autoencoders can also be used in dimensionality reduction applications, even though they are widely used in other popular applications such as image denoising, image generation, image colorization, image compression, image super-resolution, etc.
Earlier, we compared the performance of autoencoders in dimensionality reduction against PCA by training the models on the very large MNIST dataset. There, the autoencoder model easily outperformed the PCA model [ref¹] because the MNIST data is large and non-linear.
ref¹: How Autoencoders Outperform PCA in Dimensionality Reduction
Autoencoders work well with large and non-linear…
