Heterogeneous sets in dimensionality reduction and ensemble learning

We present a general framework for dealing with set heterogeneity in data and learning problems, which is able to exploit low complexity components.
The main ingredients are (i) a definition of complexity for elements of a convex union that takes into account the complexities of their individual composition -- this is used to cover the heterogeneous convex union, and (ii) upper bounds on the complexities of restricted subsets. We demonstrate this approach in two different application areas, highlighting their conceptual connection. (1) In random projection based dimensionality reduction, we obtain improved bounds on the uniform preservation of Euclidean norms and distances when low complexity components are present in the union. (2) In statistical learning, our generalisation bounds justify heterogeneous ensemble learning methods that were incompletely understood before. We exemplify empirical results with boosting type random subspace and random projection ensembles that implement our bounds.