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- Max Planck SocietyDE12 papers
- Data61AU2 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
- Max Planck Institute for Intelligent SystemsDE2 papers
- University of Central FloridaUS2 papers
- California Institute of TechnologyUS1 paper
- Carnegie Mellon UniversityUS1 paper
- Centre de Recerca MatemàticaES1 paper
- Fraunhofer-GesellschaftDE1 paper
- Google (United States)US1 paper
- Graz University of TechnologyAT1 paper
- LMU KlinikumDE1 paper
5 papers · 1 filter
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Hannes Nickisch, Carl Edward Rasmussen
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi…
Clustering Stability: An Overview
Ulrike von Luxburg
A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most s…
Expectation Propagation on the Maximum of Correlated Normal Variables
Philipp Hennig
Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two post…
Telling cause from effect based on high-dimensional observations
Dominik Janzing, Patrik O. Hoyer, Bernhard Schoelkopf
We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occ…
How the initialization affects the stability of the k-means algorithm
Sebastien Bubeck, Marina Meila, Ulrike von Luxburg
We investigate the role of the initialization for the stability of the k-means clustering algorithm. As opposed to other papers, we consider the actual k-means algorithm and do not…