Clustering and Anomaly Detection in Financial Trading Data
In this thesis we propose a new form of Variational Autoencoder called the Conditional Latent Space Variational Autoencoder or CL-VAE. By conditioning on a known label in a dataset we can decide what points are being mapped to what prior distribution. This makes the latent space more understandable and separates the classes further. It also subverts the tug-of-war effect between reconstruction los
