Clustering using Max-norm Constrained Optimization

Ali Jalali, Nathan Srebro

Abstract: We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-norm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other approaches.