Add to ORKGAbstract — Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been proposed that an information theoretic measure may be used as a cost function for clustering in a kernel space, approximated by the spectral properties of the Laplacian matrix. In this paper we extend this result to other kernel matrices. We develop an algorithm for the actual clustering which is based on comparing angles between data points, and demonstrate that the proposed method performs equally good as a state-of-the art spectral clustering method. We point out some drawbacks of spectral clustering related to outliers, and suggest measures to be taken....
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
Abstract. Spectral methods have received attention as powerful theoretical and prac-tical approaches...
Motivated by multi-distribution divergences, which orig-inate in information theory, we propose a no...
Motivated by multi-distribution divergences, which originate in information theory, we propose a not...
Motivated by multi-distribution divergences, which originate in information theory, we propose a not...
Abstract A new distance measure between probability density functions (pdfs) is introduced, which we...
The recent years have seen a surge of interest in spectral-based methods and kernel-based methods fo...
Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an ...
Despite many empirical successes of spectral clustering methods-algorithms that cluster points using...
Spectral clustering is a powerful technique in clustering specially when the structure of data is no...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
Abstract. Spectral methods have received attention as powerful theoretical and prac-tical approaches...
Motivated by multi-distribution divergences, which orig-inate in information theory, we propose a no...
Motivated by multi-distribution divergences, which originate in information theory, we propose a not...
Motivated by multi-distribution divergences, which originate in information theory, we propose a not...
Abstract A new distance measure between probability density functions (pdfs) is introduced, which we...
The recent years have seen a surge of interest in spectral-based methods and kernel-based methods fo...
Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an ...
Despite many empirical successes of spectral clustering methods-algorithms that cluster points using...
Spectral clustering is a powerful technique in clustering specially when the structure of data is no...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...
International audienceThis article proposes a first analysis of kernel spectral clustering methods i...