Ulrike von Luxburg

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 31 Citations 14,388 96 World Ranking 9463 National Ranking 453

Research.com Recognitions

Awards & Achievements

2020 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Informatics

Overview

What is she best known for?

The fields of study Ulrike von Luxburg is best known for:

Random graph
Cluster analysis
Metric space

In her study, Submanifold and Pointwise is strongly linked to Mathematical analysis, which falls under the umbrella field of Laplace operator. Her research combines Mathematical analysis and Pointwise. Her study ties her expertise on Consistency (knowledge bases) together with the subject of Discrete mathematics. Her research on Consistency (knowledge bases) frequently links to adjacent areas such as Discrete mathematics. She combines Cluster analysis and CURE data clustering algorithm in her research. In her works, she conducts interdisciplinary research on CURE data clustering algorithm and Canopy clustering algorithm. Ulrike von Luxburg performs integrative study on Canopy clustering algorithm and Nearest-neighbor chain algorithm. Her Artificial intelligence study frequently links to related topics such as Spectral clustering. As part of her studies on Spectral clustering, she frequently links adjacent subjects like Artificial intelligence.

Her most cited work include:

Consistency of spectral clustering (457 citations)
From Graphs to Manifolds – Weak and Strong Pointwise Consistency of Graph Laplacians (236 citations)
A Sober Look at Clustering Stability (172 citations)

What are the main themes of her work throughout her whole career to date

Artificial intelligence and Data mining are two areas of study in which Ulrike von Luxburg engages in interdisciplinary research. She brings together Data mining and Artificial intelligence to produce work in her papers. She integrates many fields, such as Discrete mathematics and Mathematical analysis, in her works. She combines Mathematical analysis and Combinatorics in her studies. In her works, she undertakes multidisciplinary study on Combinatorics and Random graph. Her research on Graph often connects related topics like Theoretical computer science. Her Theoretical computer science study frequently draws connections between related disciplines such as Graph. In her papers, Ulrike von Luxburg integrates diverse fields, such as Statistics and Discrete mathematics. Ulrike von Luxburg performs multidisciplinary study on Cluster analysis and k-nearest neighbors algorithm in her works.

Ulrike von Luxburg most often published in these fields:

Artificial intelligence (54.05%)
Discrete mathematics (45.95%)
Graph (43.24%)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A tutorial on spectral clustering

Ulrike Luxburg.
Statistics and Computing (2007)

10280 Citations

From graphs to manifolds – weak and strong pointwise consistency of graph laplacians

Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg.
conference on learning theory (2005)

359 Citations

A sober look at clustering stability

Shai Ben-David;Ulrike von Luxburg;Dávid Pál.
conference on learning theory (2006)

293 Citations

Consistency of spectral clustering

Ulrike von Luxburg;Mikhail Belkin;Olivier Bousquet.
arXiv: Statistics Theory (2008)

289 Citations

Clustering Stability: An Overview

Ulrike von Luxburg.
(2010)

280 Citations

Graph Laplacians and their Convergence on Random Neighborhood Graphs

Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg.
Journal of Machine Learning Research (2007)

277 Citations

Influence of graph construction on graph-based clustering measures

Markus Maier;Ulrike V. Luxburg;Matthias Hein.
neural information processing systems (2008)

207 Citations

Distance--Based Classification with Lipschitz Functions

Ulrike von Luxburg;Olivier Bousquet.
conference on learning theory (2004)

176 Citations

Limits of Spectral Clustering

Ulrike V. Luxburg;Olivier Bousquet;Mikhail Belkin.
neural information processing systems (2004)

151 Citations

Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters

Markus Maier;Matthias Hein;Ulrike von Luxburg.
Theoretical Computer Science (2009)

146 Citations

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Frequent Co-Authors

External Links

Personal Website for Ulrike von Luxburg