Insightful Dimensionality Reduction with Very Low Rank Variable Subsets
Bruno Ordozgoiti, Sachith Pai, Marta Kołczyńska · 2021
Dimensionality reduction techniques can be employed to produce robust, cost-effective predictive models, and to enhance interpretability in exploratory data analysis. However, the models produced by many of these methods are formulated in terms of abstract factors or are too high-dimensional to facilitate insight and fit within low computational budgets.