A comparison of dimensionality reduction methods for large biological data

Abstract

Large-scale data often suffer from the curse of dimensionality and the constraints associated with it; therefore, dimensionality reduction methods are often performed prior to most machine learning pipelines. In this paper, we directly compare autoencoders performance as a dimensionality reduction technique (via the latent space) to other established methods: PCA, LASSO, and t-SNE. To do so, we use four distinct datasets that vary in the types of features, metadata, labels, and size to robustly compare different methods. We test prediction capability using both Support Vector Machines (SVM) and Random Forests (RF). Significantly, we conclude that autoencoders are an equivalent dimensionality reduction architecture to the previously established methods, and often outperform them in both prediction accuracy and time performance when condensing large, sparse datasets.