Add to ORKGWe 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.
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
We consider approximating distributions within the framework of optimal mass transport and specializ...
Abstract. We consider approximating distributions within the framework of optimal mass transport and...
ABSTRACT. In this paper, we initiate the study of exact recovery conditions for convex relaxations o...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise grou...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
Recently, Bilu and Linial [6] formalized an implicit assumption often made when choosing a clusterin...
This article proposes a constrained clustering algorithm with competitive performance and less compu...
Clustering is often formulated as a discrete optimization problem. The objective is to find, among a...
We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, w...
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering, a...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
Recently, Bilu and Linial [10] formalized an implicit assumption often made when choosing a clus-ter...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
We consider approximating distributions within the framework of optimal mass transport and specializ...
Abstract. We consider approximating distributions within the framework of optimal mass transport and...
ABSTRACT. In this paper, we initiate the study of exact recovery conditions for convex relaxations o...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise grou...
K-Means clustering still plays an important role in many computer vision problems. While the convent...
Recently, Bilu and Linial [6] formalized an implicit assumption often made when choosing a clusterin...
This article proposes a constrained clustering algorithm with competitive performance and less compu...
Clustering is often formulated as a discrete optimization problem. The objective is to find, among a...
We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, w...
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering, a...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
Recently, Bilu and Linial [10] formalized an implicit assumption often made when choosing a clus-ter...
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (...
We consider approximating distributions within the framework of optimal mass transport and specializ...
Abstract. We consider approximating distributions within the framework of optimal mass transport and...