Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Published in IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2024
An out-of-sample extension method for the random forest-based supervised dimensionality reduction method, RF-PHATE, combining information learned from the random forest model with the function-learning capabilities of autoencoders.