资源分类
》 资源 》论文》Clustering using Max-norm Constrained Optimization

Clustering using Max-norm Constrained Optimization

2020-03-02 | |  76 |   49 |   0

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 nuclearnorm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other approaches.

上一篇：Copula Mixture Model for Dependency-seeking Clustering

下一篇：Finding Botnets Using Minimal Graph Clusterings

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...