K
Kerstin Bunte
Researcher at University of Groningen
Publications - 74
Citations - 1301
Kerstin Bunte is an academic researcher from University of Groningen. The author has contributed to research in topics: Learning vector quantization & Dimensionality reduction. The author has an hindex of 16, co-authored 66 publications receiving 1142 citations. Previous affiliations of Kerstin Bunte include University of Rochester & Helsinki Institute for Information Technology.
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Journal ArticleDOI
Limited Rank Matrix Learning, discriminative dimension reduction and visualization
Kerstin Bunte,Petra Schneider,Barbara Hammer,Frank-Michael Schleif,Thomas Villmann,Michael Biehl +5 more
TL;DR: An extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm to matrices of limited rank corresponding to low-dimensional representations of the data to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently.
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A general framework for dimensionality-reducing data visualization mapping
TL;DR: This letter proposes a general view on nonparametric dimension reduction based on the concept of cost functions and properties of the data such that direct out-of-sample extensions become possible and offers the possibility of investigating the generalization ability of data visualization to new data points.
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Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences
TL;DR: The divergence which measures the difference between probability distributions in the original and the embedding space can be treated independently from other components like, e.g. the similarity of data points or the data distribution.
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Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
Solveig K. Sieberts,Zhu Fan,Javier Garcia-Garcia,Eli A. Stahl,Abhishek Pratap,Gaurav Pandey,Dimitrios A. Pappas,Daniel Aguilar,Bernat Anton,Jaume Bonet,Ridvan Eksi,Oriol Fornes,Emre Guney,Hong-Dong Li,Manuel Alejandro Marín,Bharat Panwar,Joan Planas-Iglesias,Daniel Poglayen,Jing Cui,André O. Falcão,Christine Suver,Bruce Hoff,Venkatachalapathy S. K. Balagurusamy,Donna N. Dillenberger,Elias Chaibub Neto,Thea Norman,Tero Aittokallio,Muhammad Ammad-ud-din,Muhammad Ammad-ud-din,Chloé-Agathe Azencott,Victor Bellon,Valentina Boeva,Kerstin Bunte,Kerstin Bunte,Himanshu Chheda,Lu Cheng,Lu Cheng,Lu Cheng,Jukka Corander,Jukka Corander,Michel Dumontier,Anna Goldenberg,Peddinti Gopalacharyulu,Mohsen Hajiloo,Daniel Hidru,Alok Jaiswal,Samuel Kaski,Samuel Kaski,Samuel Kaski,Beyrem Khalfaoui,Suleiman A. Khan,Suleiman A. Khan,Suleiman A. Khan,Eric R. Kramer,Pekka Marttinen,Pekka Marttinen,Aziz M. Mezlini,Bhuvan Molparia,Matti Pirinen,Janna Saarela,Matthias Samwald,Véronique Stoven,Hao Tang,Jing Tang,Ali Torkamani,Jean Phillipe Vert,Bo Wang,Tao Wang,Krister Wennerberg,Nathan E. Wineinger,Guanghua Xiao,Yang Xie,Rae S. M. Yeung,Xiaowei Zhan,Cheng Zhao,Jeff Greenberg,Joel M. Kremer,Kaleb Michaud,Anne Barton,Marieke J H Coenen,Xavier Mariette,Corinne Miceli,Nancy A. Shadick,Michael E. Weinblatt,Niek de Vries,Paul P. Tak,Danielle M. Gerlag,Tom W J Huizinga,Fina A S Kurreeman,Cornelia F Allaart,S. Louis Bridges,Lindsey A. Criswell,Larry W. Moreland,Lars Klareskog,Saedis Saevarsdottir,Leonid Padyukov,Peter K. Gregersen,Stephen H. Friend,Robert M. Plenge,Gustavo Stolovitzky,Baldo Oliva,Yuanfang Guan,Lara M. Mangravite +102 more
TL;DR: Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
Regularization in Matrix Relevance Learning
TL;DR: In this paper, a regularization technique was proposed to extend matrix learning schemes in learning vector quantization (LVQ), which extended the concept of adaptive distance measures in LVQ to the use of relevance matrices.