University of Natural Resources and Life Sciences, Vienna (BOKU) - Research portal

Go directly to the nagivation.
Go directly to the content.

Navigation/Search

Selected Publication:

Izquierdo-Verdiguier, E; Jenssen, R; Gomez-Chova, L; Camps-Valls, G.
(2015): Spectral clustering with the probabilistic cluster kernel
NEUROCOMPUTING. 2015; 149: 1299-1304. FullText FullText_BOKU

Abstract:: This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets. (c) 2014 Elsevier B.V. All rights reserved.
Authors BOKU Wien:: Izquierdo-Verdiguier Emma
Find related publications in this database (Keywords): Kernel methods; Generative kernels; Manifold learning; Spectral clustering