Hands-On Machine Learning for Algorithmic Trading

Sparsity constraints with regularized autoencoders

The powerful capabilities of neural networks to represent complex functions require tight limitations of the capacity of the encoder and decoder to force the extraction of a useful signal rather than noise. In other words, when it is too easy for the network to recreate the input, it fails to learn only the most interesting aspects of the data.

This challenge is similar to the overfitting phenomenon that frequently occurs when using models with a high capacity for supervised learning. Just as in these settings, regularization can help by adding constraints to the autoencoder that facilitate the learning of a useful representation.

A common approach that we explore later is the use of L1 regularization, ...