Publications

2017

  • Consortium
    Cell Systems
    A community-based challenge for building predictive models of gene essentialities over a large-scale functional screen of cancer cell lines
    Notes: in press

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  • S. Ghosh, E. S. Baranowski, R. v. Veen, G. d. Vries, M. Biehl, W. Arlt, P. Tino, K. Bunte and et al
    Proc. of the 25th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 177-186
    Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders

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  • E. Baranowski, K. Bunte, C. H. Shackleton, A. E. Taylor, B. A. Hughes, M. Biehl, P. Tino, T. Guran and et al
    Endocrine Abstracts
    Steroid metabolomics for accurate and rapid diagnosis of inborn steroidogenic disorders

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2016

  • F. Aiolli, K. Bunte, R. Hérault and M. Kanevski
    Neurocomputing, vol. 192, pp. 1-2
    Advances in artificial neural networks, machine learning and computational intelligence Selected papers from the 23rd European Symposium on Artificial Neural Networks (ESANN 2015)

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  • S. Sieberts, F. Zhu, J. García-García, E. Stahl, A. Pratap, G. Pandey, D. Pappas, D. Aguilar and et al
    Nature Communications, vol. 7 (NCOMMS-15-15784D)
    Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

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  • K. Bunte, M. Kaden and F. Schleif
    Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM, eds. Merényi, Erzsébet and Mendenhall, J. Michael and O'Driscoll, Patrick, Houston, Texas, USA, pp. 341-353
    Low-Rank Kernel Space Representations in Prototype Learning

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  • K. Bunte, E. S. Baranowski, W. Arlt and P. Tino
    New Challenges in Neural Computation (NC²), eds. Barbara Hammer and Thomas Martinetz and Thomas Villmann, Hannover, Germany, pp. 20-23
    Relevance Learning Vector Quantization in Variable Dimensional Spaces

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  • K. Bunte, P. Tino and W. Arlt
    Relevance learning vector quantization in variable dimensional spaces
    Notes: Patent GB 1615330.6

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  • K. Bunte, E. Leppäaho, I. Saarinen and S. Kaski
    Bioinformatics, vol. 32 (16), pp. 2457-2463
    Sparse group factor analysis for biclustering of multiple data sources

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  • E. S. Baranowski, K. Bunte, C. H. Shackleton, A. E. Taylor, B. A. Hughes, M. Biehl, P. Tino, T. Guran and et al
    Society for Endocrinology BES 2016
    Steroid metabolomics for diagnosis of inborn steroidogenic disorders - bridging the gap between clinician and scientist through computational approaches

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2015

  • D. Archambault, K. Bunte, M. Á. Carreira-Perpiñán, D. Ebert, T. Ertl and B. Zupan
    Dagstuhl Reports: Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101), vol. 5, pp. 7
    4.2 Machine Learning Meets Visualization: A Roadmap for Scalable Data Analytics

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  • J. A. Lee and K. Bunte
    Unsupervised dimensionality reduction: the challenge of big data visualisation

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2014

  • M. Strickert, K. Bunte, F. Schleif and E. Hüllermeier
    Neurocomputing, vol. 141, pp. 97-109
    Correlation-based embedding of pairwise score data

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  • K. Bunte, M. Järvisalo, J. Berg, P. Myllymäki, J. Peltonen and S. Kaski
    Proc. of the 28th Conference on Artificial Intelligence (AAAI), eds. Carla E. Brodley and Peter Stone, Québec, Canada, pp. 1694-1700
    Optimal neighborhood preserving visualization by maximum satisfiability

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2013

  • M. Biehl, K. Bunte and P. Schneider
    PLOS ONE, vol. 8 (3), pp. e59401
    Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization

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  • N. Aghaeepour, G. Finak, FlowCAP Consortium, DREAM Consortium, H. Hoos, T. R. Mosmann, R. Brinkman, R. Gottardo and et al
    Nature Methods, vol. 10 (3), pp. 228-238
    Critical assessment of automated flow cytometry data analysis techniques

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  • M. Strickert and K. Bunte
    Proc. of the 21th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 77-82
    Soft Rank Neighbor Embeddings

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2012

  • K. Bunte, M. Biehl and B. Hammer
    Neural Computation, vol. 24 (3), pp. 771-804
    A general framework for dimensionality reducing data visualization using explicit mapping functions

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  • K. Bunte
    Künstliche Intelligenz, vol. 26 (4), pp. 417-418
    Adaptive Dissimilarity Measures, Dimension Reduction and Visualization (University of Groningen)

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  • I. Giotis, K. Bunte, N. Petkov and M. Biehl
    Journal of Mathematical Imaging and Vision, vol. 12, pp. 1-14
    Adaptive Matrices and Filters for Color Texture Classification

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  • K. Bunte, F. Schleif and M. Biehl
    Proc. of the 20th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 387-392
    Adaptive learning for complex-valued data

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  • A. Schulz, A. Gisbrecht, K. Bunte and B. Hammer
    New Challenges in Neural Computation (NC²), eds. Barbara Hammer and Thomas Villmann, Graz, Austria, pp. 73-83
    How to visualize a classifier?

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  • M. Biehl, K. Bunte, B. Hammer, F. Schleif, P. Schneider and T. Villmann
    Proc. of the International Joint Conference on Neural Networks (IJCNN), eds. Hussein Abbass, Daryl Essam and Ruhul Sarker, Brisbane, Australia, pp. 1873-1880
    Large Margin Linear Discriminative Visualization by Matrix Relevance Learning

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  • K. Bunte, P. Schneider, B. Hammer, F. Schleif, T. Villmann and M. Biehl
    Neural Networks, vol. 26 (4), pp. 159-173
    Limited Rank Matrix Learning - Discriminative Dimension Reduction and Visualization

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  • K. Bunte, S. Haase, M. Biehl and T. Villmann
    Neurocomputing, vol. 90 (1), pp. 23-45
    Stochastic Neighbor Embedding (SNE) for Dimension Reduction and Visualization using arbitrary Divergences

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  • M. B. Huber, K. Bunte, M. B. Nagarajan, M. Biehl, L. A. Ray and A. Wismüller
    Artificial Intelligence in Medicine, vol. 56 (2), pp. 91-97
    Texture Feature Ranking with Relevance Learning to Classify Interstitial Lung Disease Patterns

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  • G. Peters, K. Bunte, M. Strickert, M. Biehl and T. Villmann
    Proc. of the 3rd Workshop on Trustworthy Self-Organizing Systems (TSOS), Paris, France, pp. 244-249
    Visualization of Processes in Self-Learning Systems

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2011

  • B. Hammer, M. Biehl, K. Bunte and B. Mokbel
    Advances in Self-Organizing Maps, WSOM 2011, eds. Jorma Laaksonen and Timo Honkela, pp. 277-287
    A general framework for dimensionality reduction for large data sets

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  • K. Bunte, I. Giotis, N. Petkov and M. Biehl
    14th International Conference on Computer Analysis of Images and Patterns (CAIP), Seville, Spain, pp. 489-497
    Adaptive Matrices for Color Texture Classification

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  • K. Bunte, M. Biehl and B. Hammer
    IEEE Symposium Series in Computational Intelligence (SSCI) 2011: Computational Intelligence and Data Mining (CIDM), Paris, France, pp. 349-356
    Dimensionality reduction mappings

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  • K. Bunte, M. Biehl, M. F. Jonkman and N. Petkov
    Pattern Recognition, vol. 44 (9), pp. 1892-1902
    Learning effective color features for content based image retrieval in dermatology

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  • K. Bunte, F. Schleif, S. Haase and T. Villmann
    Proc. of the 19th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 29-34
    Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization

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  • K. Bunte, B. Hammer, T. Villmann, M. Biehl and A. Wismüller
    Neurocomputing, vol. 74 (9), pp. 1340-1350
    Neighbor Embedding XOM for Dimension Reduction and Visualization

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  • K. Bunte, M. Biehl and B. Hammer
    Proc. of the 19th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 281-286
    Supervised dimension reduction mappings

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  • M. B. Huber, K. Bunte, M. B. Nagarajan, M. Biehl, L. A. Ray and A. Wismüller
    SPIE Medical Imaging: Computer-Aided Diagnosis, eds. Ronald M. Summers M.D. and Bram van Ginneken
    Texture feature selection with relevance learning to classify interstitial lung disease patterns

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  • G. Papari, K. Bunte and M. Biehl
    Waypoint averaging and step size control in learning by gradient descent

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2010

  • K. Bunte, B. Hammer, A. Wismüller and M. Biehl
    Neurocomputing, vol. 73 (7-9), pp. 1074-1092
    Adaptive Local Dissimilarity Measures for Discriminative Dimension Reduction of Labeled Data

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  • K. Bunte, B. Hammer, T. Villmann, M. Biehl and A. Wismüller
    Proc. of the 18th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 87-92
    Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization

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  • K. Bunte, S. Haase, M. Biehl and T. Villmann
    Mathematical Foundations of Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization

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  • P. Schneider, K. Bunte, B. Hammer and M. Biehl
    IEEE Transactions on Neural Networks, vol. 21 (5), pp. 831-840
    Regularization in Matrix Relevance Learning

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  • B. Hammer, K. Bunte and M. Biehl
    Learning paradigms in dynamic environments, eds. B. Hammer and P. Hitzler and W. Maas and M. Toussaint, pp. 15
    Some steps towards a general principle for dimensionality reduction mappings

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2009

  • K. Bunte, M. Biehl, N. Petkov and M. F. Jonkman
    Proc. of the 17th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 129-134
    Adaptive Metrics for Content Based Image Retrieval in Dermatology

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  • K. Bunte, B. Hammer and M. Biehl
    13th International Conference on Computer Analysis of Images and Patterns (CAIP), eds. Xiaoyi Jiang and Nicolai Petkov, Münster, Germany, pp. 1162-1170
    Nonlinear Dimension Reduction and Visualization of Labeled Data

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  • K. Bunte, B. Hammer, P. Schneider and M. Biehl
    Proc. of the 17th European Symposium on Artificial Neural Networks (ESANN), eds. M. Verleysen, Bruges, Belgium, pp. 65-70
    Nonlinear Discriminative Data Visualization

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2008

  • K. Bunte, P. Schneider, B. Hammer, F. Schleif, T. Villmann and M. Biehl
    Discriminative Visualization by Limited Rank Matrix Learning

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  • P. Schneider, K. Bunte, B. Hammer, T. Villmann and M. Biehl
    Regularization in matrix relevance learning

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2007

  • T. Hermann, K. Bunte and H. Ritter
    Proc. of the 13th International Conference on Auditory Display (ICAD), Montréal, Canada, pp. 26-29
    Relevance-based Interactive Optimization of Sonification

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