Timezone: »

Robust Clustering as Ensembles of Affinity Relations
Hairong Liu · Longin Jan J Latecki · shuicheng yan

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @

In this paper, we regard clustering as ensembles of k-ary affinity relations and clusters correspond to subsets of objects with maximal average affinity relations. The average affinity relation of a cluster is relaxed and well approximated by a constrained homogenous function. We present an efficient procedure to solve this optimization problem, and show that the underlying clusters can be robustly revealed by using priors systematically constructed from the data. Our method can automatically select some points to form clusters, leaving other points un-grouped; thus it is inherently robust to large numbers of outliers, which has seriously limited the applicability of classical methods. Our method also provides a unified solution to clustering from k-ary affinity relations with k ≥ 2, that is, it applies to both graph-based and hypergraph-based clustering problems. Both theoretical analysis and experimental results show the superiority of our method over classical solutions to the clustering problem, especially when there exists a large number of outliers.

Author Information

Hairong Liu (Baidu AI Lab)
Longin Jan J Latecki (Temple University)
shuicheng yan (National University of Singapore)

More from the Same Authors