Dr. Kai Labusch

Wissenschaftlicher Mitarbeiter

Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck

Email:
Phone:
+49 451 500 5501
Fax:
+49 451 500 5502

Research interests 

  • Learning of sparse codes
  • Feature extraction using sparse codes
  • Fast and simple algorithms for supervised learning (SVM)

Publikationen

2012

  • Hocke, Jens and Labusch, Kai and Barth, Erhardt and Martinetz, Thomas: Sparse Coding and Selected Applications. KI - Künstliche Intelligenz, no. 26, pp. 349-355, Springer Berlin / Heidelberg, 2012
    BibTeX Link
    @article{HoLaBaMa12,
    author = {Hocke, Jens and Labusch, Kai and Barth, Erhardt and Martinetz, Thomas},
    title = {Sparse {C}oding and {S}elected {A}pplications},
    publisher = {Springer Berlin / Heidelberg},
    journal = {KI - K\"unstliche Intelligenz},
    number = {4},
    volume = {26},
    pages = {349--355},
    year = {2012},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/HoLaBaMa12.pdf}
    }
    
    

2011

  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Soft-competitive Learning of Sparse Codes and its Application to Image Reconstruction. Neurocomputing, no. 74, pp. 1418-1428, 04, 2011
    BibTeX Link
    @article{LaBaMa11,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    title = {Soft-competitive {L}earning of {S}parse {C}odes and its {A}pplication to {I}mage {R}econstruction},
    journal= {Neurocomputing},
    volume = {74},
    number = {9},
    month = {04},
    pages = {1418--1428},
    year = {2011},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa11.pdf}
    }
    
    
  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent. IEEE Transactions on Selected Topics in Signal Processing, no. 5, pp. 1048-1060, 2011
    BibTeX Link
    @article{LaBaMa11b,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    journal = {IEEE Transactions on Selected Topics in Signal Processing},
    title = {{R}obust and {F}ast {L}earning of {S}parse {C}odes {W}ith {S}tochastic {G}radient {D}escent},
    year = {2011},
    volume = {5},
    pages = {1048--1060},
    number = {5},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa11b.pdf}
    }
    
    

2010

  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Bag of Pursuits and Neural Gas for Improved Sparse Coding. in Proceedings of the 19th International Conference on Computational Statistics, pp. 327-336, Springer, 2010
    BibTeX Link
    @inproceedings{LaBaMa10b,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    title = {{B}ag of {P}ursuits and {N}eural {G}as for {I}mproved {S}parse {C}oding},
    editor = {Gilbert Saporta},
    booktitle = {Proceedings of the 19th International Conference on Computational Statistics},
    publisher = {Springer},
    pages = {327--336},
    year = {2010},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa10b.pdf}
    }
    
    
  • Ingrid Braenne and Kai Labusch and Thomas Martinetz and Amir Madany Mamlouk: Interpretive Risk Assessment on GWA Data with Sparse Linear Regression. Machine Learning Reports, pp. 61-68, 2010
    BibTeX Link
    @article{BrLaMaMa10,
    author = {Ingrid Br{\ae}nne and Kai Labusch and Thomas Martinetz and Amir Madany Mamlouk},
    title = {Interpretive {R}isk {A}ssessment on {GWA} {D}ata with {S}parse {L}inear {R}egression},
    journal = {Machine Learning Reports},
    pages = {61--68},
    year = {2010},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/BrLaMaMa10.pdf}
    }
    
    
  • Kai Labusch and Thomas Martinetz: Learning Sparse Codes for Image Reconstruction. in Proceedings of the 18th European Symposium on Artificial Neural Networks, pp. 241-246, D-Side Publishers, 2010
    BibTeX Link
    @inproceedings{LaMa10,
    author = {Kai Labusch and Thomas Martinetz},
    title = {{L}earning {S}parse {C}odes for {I}mage {R}econstruction},
    booktitle = {Proceedings of the 18th European Symposium on Artificial Neural Networks},
    publisher = {D-Side Publishers},
    year = {2010},
    pages = {241--246},
    editor = {Michel Verleysen},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaMa10.pdf}
    }
    
    
  • Ingrid Braenne and Kai Labusch and Amir Madany Mamlouk: Sparse Coding for Feature Selection on Genome-wide Association Data. in Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki,Greece, September 15-18, 2010, Proceedings, no. 6352, pp. 337-346, Springer, 2010
    BibTeX Link
    @inproceedings{BrLaMa10,
    author = {Ingrid Br{\ae}nne and Kai Labusch and Amir Madany Mamlouk},
    title = {{S}parse {C}oding for {F}eature {S}election on {G}enome-wide {A}ssociation {D}ata},
    booktitle = {Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki,Greece, September 15-18, 2010, Proceedings},
    publisher = {Springer},
    volume = {6352},
    series = {Lecture Notes in Computer Science},
    pages = {337--346},
    year = {2010},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/BrLaMa10.pdf}
    }
    
    

2009

  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. in Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009, no. 5629, pp. 145-153, Springer, series Lecture Notes in Computer Science, 2009
    BibTeX Link
    @inproceedings{LaBaMa09a,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    title = {{A}pproaching the {T}ime {D}ependent {C}ocktail {P}arty {P}roblem with {O}nline {S}parse {C}oding {N}eural {G}as},
    editor = {J.C. Principe and R. Miikkulainen},
    booktitle = {Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009},
    publisher = {Springer}, series = {Lecture Notes in Computer Science},
    volume = {5629},
    pages = {145--153},
    year = {2009},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SparseCodingNeuralGas.html},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa09a.pdf}
    }
    
    
  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Demixing Jazz-Music: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. Neural Network World, no. 19, pp. 561-579, Institute of Information and Computer Technology ASCR; Faculty of Transport, Czech Polytechnic University, Prague, 2009
    BibTeX Link
    @article{LaBaMa09b,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    journal = {Neural Network World},
    publisher = {Institute of Information and Computer Technology ASCR; Faculty of Transport, Czech Polytechnic University, Prague},
    title = {{D}emixing {J}azz-{M}usic: {S}parse {C}oding {N}eural {G}as for the {S}eparation of {N}oisy {O}vercomplete {S}ources},
    volume = {19},
    number = {5},
    year = {2009},
    pages = {561--579},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa09b.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SparseCodingNeuralGas.html}
    }
    
    
  • Thomas Martinetz and Kai Labusch and Daniel Schneegass: SoftDoubleMaxMinOver: Perceptron-like Training of Support Vector Machines. IEEE Transactions on Neural Networks, no. 20, pp. 1061-1072, 2009
    BibTeX Link
    @article{MaLaSc09,
    author = {Thomas Martinetz and Kai Labusch and Daniel Schneega{\ss}},
    journal = {IEEE Transactions on Neural Networks},
    title = {{S}oft{D}ouble{M}ax{M}in{O}ver: {P}erceptron-like {T}raining of {S}upport {V}ector {M}achines},
    year = {2009},
    volume = {20},
    number = {7},
    pages = {1061--1072},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SoftDoubleMaxMinOver.html},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/MaLaSc09.pdf}
    }
    
    
  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Sparse Coding Neural Gas: Learning of Overcomplete Data Representations. Neurocomputing, no. 72, pp. 1547-1555, 2009
    BibTeX Link
    @article{LaBaMa09,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    journal = {Neurocomputing},
    title = {{S}parse {C}oding {N}eural {G}as: {L}earning of {O}vercomplete {D}ata {R}epresentations},
    year = {2009},
    volume = {72},
    number = {7--9},
    pages = {1547--1555},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa09.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SparseCodingNeuralGas.html}
     }
    
    

2008

  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Learning data representations with Sparse Coding Neural Gas. in Proceedings of the 16th European Symposium on Artificial Neural Networks, pp. 233-238, D-Side Publishers, 2008
    BibTeX Link
    @inproceedings{LaBaMa08,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    title = {Learning data representations with Sparse Coding Neural Gas},
    booktitle = {Proceedings of the 16th European Symposium on Artificial Neural Networks},
    publisher = {D-Side Publishers},
    year = {2008},
    editor = {Michel Verleysen},
    pages = {233--238},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa08.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SparseCodingNeuralGas.html}
    }
    
    
  • Kai Labusch and Fabian Timm and Thomas Martinetz: Simple Incremental One-Class Support Vector Classification. in Pattern Recognition - Proceedings of the DAGM, pp. 21-30, 2008
    BibTeX Link
    @inproceedings{LaTiMa08,
    author = {Kai Labusch and Fabian Timm and Thomas Martinetz},
    title = {Simple Incremental One-Class {S}upport {V}ector Classification},
    booktitle = {Pattern Recognition - Proceedings of the DAGM},
    editor = {Gerhard Rigoll},
    series = {Lecture Notes in Computer Science},
    pages = {21--30},
    year = {2008},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaTiMa08.pdf}
    }
    
    
  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Simple Method for High-Performance Digit Recognition Based on Sparse Coding. IEEE Transactions on Neural Networks, no. 19, pp. 1985-1989, 2008
    BibTeX Link
    @article{LaBaMa08c,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    journal = {IEEE Transactions on Neural Networks},
    title = {Simple {M}ethod for {H}igh-{P}erformance {D}igit {R}ecognition {B}ased on {S}parse {C}oding},
    year = {2008},
    volume = {19},
    number = {11},
    pages = {1985--1989},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa08c.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/DigitRecognition.html}
    }
    
    
  • Kai Labusch and Erhardt Barth and Thomas Martinetz: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. in Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II, no. 5163, pp. 788-797, Springer, 2008
    BibTeX Link
    @inproceedings{LaBaMa08b,
    author = {Kai Labusch and Erhardt Barth and Thomas Martinetz},
    title = {{S}parse {C}oding {N}eural {G}as for the {S}eparation of {N}oisy {O}vercomplete {S}ources},
    pages = {788--797},
    editor = {Vera Kurkov{\'a} and Roman Neruda and Jan Koutn\'{\i}k},
    booktitle = {Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    volume = {5163},
    year = {2008},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaBaMa08b.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SparseCodingNeuralGas.html}
    }
    
    

2007

  • Kai Labusch and Udo Siewert and Thomas Martinetz and Erhardt Barth: Learning optimal features for visual pattern recognition. in Human Vision and Electronic Imaging XII, no. 6492, Proceedings of SPIE, 2007
    BibTeX Link
    @inproceedings{LaSiMaBa07,
    author = {Kai Labusch and Udo Siewert and Thomas Martinetz and Erhardt Barth},
    title = {Learning optimal features for visual pattern recognition},
    booktitle = {Human Vision and Electronic Imaging XII},
    publisher = {Proceedings of SPIE},
    year = {2007},
    volume = {6492},
    editor = {Bernice E. Rogowitz and Thrasyvoulos N. Pappas and Scott J. Daly},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaSiMaBa07.pdf}
    }
    
    

2006

  • Daniel Schneegass and Kai Labusch and Thomas Martinetz: MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. in Artificial Neural Networks - ICANN 2006, pp. 150-158, Springer, Berlin, Heidelberg, 2006
    BibTeX Link
    @inproceedings{ScLaMa06,
    author = {Daniel Schneega{\ss} and Kai Labusch and Thomas Martinetz},
    title = {{M}ax{M}in{O}ver {R}egression: {A} {S}imple {I}ncremental {A}pproach for {S}upport {V}ector {F}unction {A}pproximation},
    booktitle = {Artificial Neural Networks - ICANN 2006},
    pages = {150--158},
    publisher = {Springer},
    address = {Berlin, Heidelberg},
    year = {2006},
    series = {Lecture Notes in Computer Science},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/ScLaMa06.pdf}
    }
    
    

2005

  • Thomas Martinetz and Kai Labusch and Daniel Schneegass: SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification.. in Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II, pp. 301-306, 2005
    BibTeX Link
    @inproceedings{MaLaSc05,
    author = {Thomas Martinetz and Kai Labusch and Daniel Schneega{\ss}},
    title = {Soft{D}ouble{M}in{O}ver: {A} {S}imple {P}rocedure for {M}aximum {M}argin {C}lassification.},
    editor = {Wlodzislaw Duch and Janusz Kacprzyk and Erkki Oja and Slawomir Zadrozny},
    series = {Lecture Notes in Computer Science},
    booktitle = {Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II},
    year = {2005},
    pages = {301--306},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SoftDoubleMaxMinOver.html},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/MaLaSc05.pdf}
    }
    
    

2004

  • Kai Labusch: MaxMinOver: Ein neues iteratives Verfahren zur Supportvektor-Klassifikation mit Anwendungen in der Gesichtserkennung. 2004
    BibTeX Link
    @MastersThesis{Labu04,
    author = {Kai Labusch},
    title = {Max{M}in{O}ver: {E}in neues iteratives {V}erfahren zur {S}upportvektor-{K}lassifikation mit {A}nwendungen in der {G}esichtserkennung},
    school = {University of L{\"u}beck},
    type = {Diploma Thesis},
    year = {2004},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/Labu04.pdf},
    url = {https://webmail.inb.uni-luebeck.de/inb-toolsdemos/SoftDoubleMaxMinOver.html}
    }
    
    

2002

  • Kai Labusch and Daniel Polani: Sensor Evolution in a Homeokinetic System. in Proceedings of the Fifth German Workshop on Artificial Life, pp. 199-208, Akademische Verlagsgesellschaft Aka, Berlin, 2002
    BibTeX Link
    @inproceedings{LaPo02,
    author = {Kai Labusch and Daniel Polani},
    title = {Sensor {E}volution in a {H}omeokinetic {S}ystem},
    booktitle = {Proceedings of the Fifth German Workshop on Artificial Life},
    pages = {199--208},
    year = {2002},
    editor = {Daniel Polani and Jan T. Kim and Thomas Martinetz},
    address = {Berlin},
    publisher = {Akademische Verlagsgesellschaft Aka},
    url = {https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/LaPo02.pdf}
    }