Clustering using Max-norm Constrained Optimization

Ali Jalali; Nathan Srebro

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arXiv · 1202.5598

Clustering using Max-norm Constrained Optimization

Ali Jalali·Nathan Srebro

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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 clustering approaches.

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Ali Jalali, Nathan Srebro. 2012-02-25. Clustering using Max-norm Constrained Optimization. https://arxiv.org/abs/1202.5598

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