Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms

Abstract

State-of-the-art subspace clustering methods are based on convex formulations whose theoretical guarantees require the subspaces to be low-dimensional. Dual Principal Component Pursuit (DPCP) is a non-convex method that is specifically designed for learning high-dimensional subspaces, such as hyperplanes. However, existing analyses of DPCP in the multi-hyperplane case lack a precise characterization of the distribution of the data and involve quantities that are difficult to interpret. Moreover, the provable algorithm based on recursive linear programming is not efficient. In this paper, we introduce a new notion of geometric dominance, which explicitly captures the distribution of the data, and derive both geometric and probabilistic conditions under which a global solution to DPCP is a normal vector to a geometrically dominant hyperplane. We then prove that the DPCP problem for a union of hyperplanes satisfies a Riemannian regularity condition, and use this result to show that a scalable Riemannian subgradient method exhibits (local) linear convergence to the normal vector of the geometrically dominant hyperplane. Finally, we show that integrating DPCP into popular subspace clustering schemes, such as K-ensembles, leads to superior or competitive performance over the state-of-the-art in clustering hyperplanes.