Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization.

ABSTRACT: Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling visualizations. To address these impediments, we introduce Algorithmic Regularization, an innovative technique for obtaining high-quality estimates of regularization paths using an iterative one-step approximation scheme. We justify our approach with a novel theoretical result, guaranteeing global convergence of the approximate path to the exact solution under easily-checked non-data-dependent assumptions. The application of algorithmic regularization to convex clustering yiel

SUBMITTER: Weylandt M 

PROVIDER: S-EPMC7518335 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature