Publications
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PhD thesis
Elżbieta Pękalska
The Dissimilarity representations in pattern recognition.
Concepts, theory and applications.
ASCI Dissertation Series no. 109.
Delft University of Technology, Delft, January 2005.
Book: 322 pages.
ISBN: 90-9019021-X.
Book on generalised kernel representations
Elżbieta Pękalska and Robert P.W. Duin
The Dissimilarity Representation for Pattern Recognition.
Foundations and Applications.
World Scientific, Singapore, December 2005.
Book: 636 pages.
ISBN: 981-256-530-2.
Buy: Amazon UK, Amazon USA, or Amazon Germany.
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TOC
Chapter 1: Introduction
Book Sections
3. R.P.W. Duin and E. Pękalska, The Science of Pattern Recognition. Achievements and Perspectives, in: Challenges for Computational Intelligence, Duch, W. and Mandziuk, J. (eds.), "Studies in Computational Intelligence" Series, Vol. 63, Springer, 221-259, 2007.
2. R.P.W. Duin and E. Pękalska, Object representation, sample size and dataset complexity (refereed), in: Basu, Mitra; Ho, Tin Kam (eds.), Data Complexity in Pattern Recognition, Springer-Verlag, pp. 25-58, 2006.
1. E. Pękalska, Introduction to multidimensional scaling, in: J. Meij (eds.), Dealing with the data flood, STT Netherlands, Study Centre for Technology Trends, The Hague, 612-628, 2002.
Refereed Journal Papers
13. R.P.W. Duin, and E.Pękalska, The dissimilarity space: between structural and statistical pattern recognition, accepted to Pattern Recognition Letters, 2011.
12. M. Bicego, E.Pękalska, D.M.J. Tax, and R.P.W. Duin, Component-based Discriminative Classification for Hidden Markov Models, Pattern Recognition, vol. 42, no. 11, 2637-2648, 2009.
11. E.Pękalska and B.Haasdonk, Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, no.6, 1017-1032, 2009.
10. P. Juszczak, D.M.J.Tax, E.Pękalska and R.P.W. Duin, Minimum spanning tree based one-class classifier, Neurocomputing, vol.72, no. 7-9, 1859-1869, 2009.
9. E.Pękalska and R.P.W. Duin, Beyond traditional kernels: classification in two dissimilarity-based representation spaces, IEEE Transactions on Systems, Man and Cybernetics--Part C, vol. 38, no. 6, 729-744, 2008.
8. A.Harol, C.Lai, E.Pękalska and R.P.W. Duin, Pairwise feature evaluation for constructing reduced representations, Pattern Analysis and Applications, vol. 10, issue 1, 55-68, 2007.
7. M. Lozano, J.M. Sotoca, J.S. Sanchez, F.Pla, E. Pękalska and R.P.W. Duin, Experimental Study on Prototype Optimisation Algorithms for Prototype-based Classification, Pattern Recognition, vol. 39, issue 10, 1827-1838, 2006.
6. E.Pękalska, R.P.W. Duin, and P. Paclik, Prototype Selection for Dissimilarity-based Classifiers, Pattern Recognition, vol. 39, issue 2, 189-208, 2006. Pattern Recognition Society Best Paper Award for 2006.
5. C. Lai, D.M.J. Tax, R.P.W. Duin, E. Pękalska and P. Paclik, A Study on Combining Image Representations for Image Classification and Retrieval, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 5, 867-890, 2004.
4. E. Pękalska and R.P.W. Duin, Dissimilarity representations allow for building good classifiers, Pattern Recognition Letters, vol. 23, no. 8, 943-956, 2002.
3. E. Pękalska, P. Paclik, and R.P.W. Duin, A Generalized Kernel Approach to Dissimilarity-based Classification, Journal of Machine Learning Research, Special Issue on Kernel Methods, vol. 2, no. 2, 175-211, 2002.
2. E. Pękalska and R.P.W. Duin, Automatic pattern recognition by similarity representations, Electronics Letters, vol. 37, no. 3, 159-160, 2001.
1. R.P.W. Duin, E. Pękalska, and D. de Ridder, Relational discriminant analysis, Pattern Recognition Letters, vol. 20, no. 11-13, 1175-1181, 1999.
Invited Conference Papers / Keynote lectures
8. R.P.W. Duin, E. Pękalska, The dissimilarity representation for structural pattern recognition, in: Cesar San Martin, Sang-Woon Kim (eds.), Proc. CIARP 2011, Lecture Notes in Computer Science, vol. 7042, Springer, 1-24, 2011.
7. B. Haasdonk and E.Pękalska, Indefinite Kernel Discriminant Analysis, invited paper, International Conference on Computational Statistics, 2010.
6. R.P.W.Duin and E.Pękalska, Structural inference of sensor-based measurements, invited talk, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Lecture Notes in Computer Science, vol. 4109, 41-55, 2006.
5. E. Pękalska and R.P.W. Duin, Learning with general proximity measures, invited talk, in: A. Fred, A.Lourenco (eds.), Workshop on Pattern Recognition in Information Systems, Paphos, Cyprus, IS15-IS24, 2006.
4. E. Pękalska and R.P.W. Duin, The use of dissimilarities for object recognition, invited talk, EOS Conference on Industrial Image and Machine Vision, EOS European Optical Society, Hannover, Germany, 50-53, 2005.
3. R.P.W.Duin and E.Pękalska, Open issues in pattern recognition, invited talk (refereed), International Conference on Computer Recognition Systems, Rydzyna, Poland, 2005, 27-42.
2. R.P.W. Duin, E. Pękalska, P. Paclik and D.M.J. Tax, The dissimilarity representation, a basis for domain based pattern recognition?, invited talk (refereed), Representations in Pattern Recognition, IAPR Workshop, Cambridge, 43-56, 2004.
1. R.P.W. Duin and E. Pękalska, Possibilities of zero-error recognition by dissimilarity representations, invited talk, in: J.M. Inesta, L. Mico (eds.), Pattern Recognition in Information Systems (Proc. PRIS2002, Alicante, April 2002), ICEIS Press, Setubal, Portugal, 20-32, 2002.
Refereed International Conference Papers
28. R.P.W. Duin, M. Loog, E. Pękalska, and D.M.J. Tax, Feature-based Dissimilarity Space Classification, Proceedings of the ICPR 2010 Contests (Classifier Domain of Competence Contest), Lecture Notes in Computer Science, Springer, 2010. Champion of the Neighborhood Dominance Test. Offline training award.
27. R.P.W. Duin and E. Pękalska, Non-Euclidean Dissimilarities: Causes and Informativeness, Proceedings of the SSPR & SPR 2010, Lecture Notes in Computer Science, vol. 6218, Springer, Heidelberg, 324-333, 2010.
26. R. Wilson, E. Hancock, E.Pękalska and R.P.W. Duin, Spherical Embeddings for non-Euclidean Dissimilarities, Proceedings of the CVPR, 1903-1910, 2010.
25. M. Bicego, M. Cristani, V. Murino, E.Pękalska, R.P.W. Duin, Clustering-Based Construction of Hidden Markov Models for Generative Kernels, EMMCVPR, 466-479, 2009.
24. B. Haasdonk and E.Pękalska, Classification with Kernel Mahalanobis Distance Classifiers, German Classification Society Annual Conference, 2008. Best paper award.
23. R.P.W. Duin, E.Pękalska, A.Harol, W.-J.Lee and H.Bunke, On Euclidean corrections for non-Euclidean dissimilarities, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, 551-561, 2008.
22. B. Haasdonk and E.Pękalska, Indefinite Kernel Fisher Discriminant, oral presentation, International Conference on Pattern Recognition, 2008.
21. R.P.W. Duin and E.Pękalska, On refining dissimilarity matrices for an improved NN learning, International Conference on Pattern Recognition, 2008.
20. M.Bicego, E.Pękalska and R.P.W. Duin, Group-induced vector spaces, oral presentation, Multiple Classifier Systems, Lecture Notes on Computer Science, vol. 4472, Prague, Czech Republic, 190-199, 2007.
19. E.Pękalska, R.P.W. Duin, Dissimilarity-based classification with vectorial representations, oral presentation, International Conference on Pattern Recognition, vol. 3, 137-140, Hong Kong, 2006.
18. E.Pękalska, A.Harol, R.P.W. Duin, B.Spillmann and H.Bunke, Non-Euclidean or non-metric measures can be informative, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Hong Kong, Lecture Notes in Computer Science, vol. 4109, 871-880, 2006.
17. B.Spillmann, M.Neuhaus, H.Bunke, E.Pękalska, R.P.W. Duin, Transforming strings to vectors spaces using prototype selection, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Hong Kong, Lecture Notes in Computer Science, vol. 4109, 287-296, 2006.
16. A.Harol, E.Pękalska, S.Verzakov, R.P.W. Duin, Augmented embedding of dissimilarity data into (pseudo-)Euclidean spaces, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Hong Kong, Lecture Notes in Computer Science, vol. 4109, 613-621, 2006.
15. D.M.J.Tax, P.Juszczak, E.Pękalska, R.P.W. Duin, Outlier detection using ball descriptions with adjustable metric, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Hong Kong, Lecture Notes in Computer Science, vol. 4109, 587-595, 2006.
14. E.Pękalska, A.Harol, C.Lai and R.P.W. Duin, Pairwise selection of features and prototypes, oral presentation, International Conference on Computer Recognition Systems, Rydzyna, Polanad, 271-278, 2005. Awarded for the best presentation.
13. E.Pękalska, R.P.W. Duin, S.Gunter and H.Bunke, On not making dissimilarities Euclidean, oral presentation, Joint IAPR International Workshops on Statistical and Structural Pattern Recognition, Lisbon, Portugal, 1143-1151, 2004.
12. R.P.W. Duin, E. Pękalska and D.M.J. Tax, The Characterization of Classification Problems by Classifier Disagreements, oral presentation, International Conference on Pattern Recognition, vol. 2, IEEE Computer Society, Los Alamitos, CA, 140-143, 2004.
11. E.Pękalska, M.Skurichina and R.P.W. Duin, Combining Dissimilarity-based One-class Classifiers, oral presentation, Multiple Classifier Systems, Lecture Notes on Computer Science, vol. 3077, Sardegna, Italy, 122-133, 2004.
10. E. Pękalska, D.M.J. Tax, and R.P.W. Duin, One-Class LP Classifiers for Dissimilarity Representations, in: S. Becker, S. Thrun and K. Obermayer (eds.), Advances in Neural Information Processing Systems, vol. 15 (Proc. NIPS 2002, Vancouver, Dec.9-12, 2002), MIT Press, Cambridge, MA, 761-768, 2003.
9. E. Pękalska and R.P.W. Duin, Spatial representation of dissimilarity data via lower-complexity linear and nonlinear mappings, oral presentation, in: T. Caelli, A. Amin, R.P.W. Duin, M. Kamel, D. de Ridder (eds.), Structural, Syntactic, and Statistical Pattern Recognition, Proc. Joint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada, Aug.6-9), Lecture Notes in Computer Science, vol. 2396, Springer Verlag, Berlin, 470-478, 2002.
8. E. Pękalska and R.P.W. Duin, Prototype Selection for Finding Efficient Representations of Dissimilarity Data, oral presentation, in: R. Kasturi, D. Laurendeau, C. Suen (eds.), ICPR16, Proceedings 16th International Conference on Pattern Recognition (Quebec City, Canada, Aug.11-15), vol. III, IEEE Computer Society Press, Los Alamitos, 37-40, 2002.
7. E. Pękalska, R.P.W. Duin, and M. Skurichina, A discussion on the classifier projection space for classifier combining, oral presentation, in: F. Roli, J. Kittler (eds.), Multiple Classifier Systems, Proceedings Third International Workshop MCS 2002 (Cagliari, Italy, June 24-26), Lecture Notes in Computer Science, vol. 2364, Springer Verlag, Berlin, 137-148, 2002.
6. D. de Ridder, E. Pękalska, and R.P.W. Duin, The Economics of Classification: Error vs. Complexity, in: R. Kasturi, D. Laurendeau, C. Suen (eds.), ICPR16, Proceedings 16th International Conference on Pattern Recognition (Quebec City, Canada, Aug.11-15), vol. II, IEEE Computer Society Press, Los Alamitos, 244-247, 2002.
5. C. Lai, D.M.J. Tax, R.P.W. Duin, E. Pękalska, and P. Paclik, On combining one-class classifiers for image database retrieval, oral presentation, in: F. Roli, J. Kittler (eds.), Multiple Classifier Systems, Proceedings Third International Workshop MCS 2002 (Cagliari, Italy, June 24-26), Lecture Notes in Computer Science, vol. 2364, Springer Verlag, Berlin, 212-221, 2002.
4. R.P.W. Duin and E. Pękalska, Complexity of Dissimilarity based Pattern Classes, in: I. Austvoll (eds.), Proceedings of the 12th Scandinavian Conference on Image Analysis, SCIA 2001 (Bergen, Norway, June 11-14), NOBIM, Stavanger, Norway, 663-670, 2001.
3. E. Pękalska and R.P.W. Duin, On Combining Dissimilarity Representations, oral presentation, in: J. Kittler, F. Roli (eds.), Multiple Classifier Systems, Proceedings Second International Workshop MCS 2001 (Cambridge, UK, July), Lecture Notes in Computer Science, vol. 2096, Springer Verlag, Berlin, 2001, 359-368.
2. E. Pękalska and R.P.W. Duin, Classifiers for dissimilarity-based pattern recognition, oral presentation, in: A. Sanfeliu, J.J. Villanueva, M. Vanrell, R. Alquezar, A.K. Jain, J. Kittler (eds.), ICPR15, Proc. 15th Int. Conference on Pattern Recognition (Barcelona, Spain, Sep.3-7), vol. 2, Pattern Recognition and Neural Networks, IEEE Computer Society Press, Los Alamitos, 12-16, 2000.
1. E. Pękalska, M. Skurichina, and R.P.W. Duin, Combining Fisher Linear Discriminants for Dissimilarity Representations, oral presentation, in: J. Kittler, F. Roli (eds.), Multiple Classifier Systems (Proc. First International Workshop, MCS 2000, Cagliari, Italy, June 2000), Lecture Notes in Computer Science, vol. 1857, Springer, Berlin, 117-126, 2000.
Conference Posters
N. van Rodijnen, E. Postma, E. Pękalska, S. Hoop, M. van Beers, M. Leers, I. Sprinkhuizen-Kuyper, M. Nap. Exploring the possibilities for automated FCM ploidy classification of paraffin embedded breast tumors. Poster presented during the ISAC (International Society for Analytical Cytology) XXII International Congress, 22-27 May 2004, Montpellier, France.
Other Conference Papers
5. C. Lai, D.M.J. Tax, R.P.W. Duin, E. Pękalska, and P. Paclik, Database retrieval: the use of combined dissimilaities, in: S. Vassiliades, L.M.J. Florack, J.W.J. Heijnsdijk, A. van der Steen (eds.), Proc. ASCI 2003, 9th Annual Conf. of the Advanced School for Computing and Imaging (Heijen, NL, June 4-6), ASCI, Delft, 2003, 177-184.
4. E. Pękalska and R.P.W. Duin, Is combining useful for dissimilarity representations?, in: R.L. Lagendijk, J.W.J. Heijnsdijk, A.D. Pimentel, M.H.F. Wilkinson (eds.), Proc. ASCI 2001, 7th Annual Conf. of the Advanced School for Computing and Imaging (Heijen, NL, May 30-June 1), ASCI, Delft, 2001, 154-161.
3. E. Pękalska and R.P.W. Duin, Classification on dissimilarity data: a first look, in: L.J. van Vliet, J.W.J. Heijnsdijk, T. Kielman, P.M.W. Knijnenburg (eds.), Proc. ASCI 2000, 6th Annual Conf. of the Advanced School for Computing and Imaging (Lommel, Belgium, June 14-16), ASCI, Delft, 2000, 221-228.
2. E. Pękalska, D. de Ridder, R.P.W. Duin, and M.A. Kraaijveld, A new method of generalizing Sammon mapping with application to algorithm speed-up, in: M. Boasson, J.A. Kaandorp, J.F.M. Tonino, M.G. Vosselman (eds.), Proc. ASCI 1999, Proc. 5th Annual Conference of the Advanced School for Computing and Imaging (Heijen, NL, June 15-17), ASCI, Delft, 1999, 221-228.
1. A. Ypma, E. Pękalska and R.P.W. Duin, Domain approximation for condition monitoring, in: B.M.ter Haar Romeny, D.H.J. Epema, J.F.M. Tonino, A.A. Wolters (eds.), Proc. ASCI 1998, 4th Annual Conf. of the Advanced School for Computing and Imaging (Lommel, Belgium, June 9-11), ASCI, Delft, 1998, 257-263.
Reports
7. R.P.W. Duin and E. Pękalska, Study on (non)geometricity, Delivarable D3.1, SIMBAD (EU,FP7,FET), 2009, 1-33.
6. R.P.W. Duin and E. Pękalska, Datasets and tools for dissimilarity analysis in pattern recognition, Technical Report 2009_9, SIMBAD (EU,FP7,FET), 2009, 1-174.
5. R.P.W. Duin and E. Pękalska, Domain based classification, Internal report, TUDelft, EEMCS,ICT, Delft, The Netherlands, 1-8, 2005.
4. G.M.P. van Kempen, P. Paclik, R. Kohlus, E. Pękalska and R.P.W. Duin, Structural and Compositional Analysis for improved design of detergent powders (PowderSCAN), Unilever Research Vlaardingen, internal report, 2001.
3. E. Pękalska, R.P.W. Duin, M.A. Kraaijveld, and D. de Ridder, Multidimensional scaling, theoretical aspects, Report for Shell- SIEP, Rijswijk, project TN-97-036, 1998, 1-47.
2. E. Pękalska, R.P.W. Duin, M.A. Kraaijveld, and D. de Ridder, Multidimensional scaling, applications to Shell data, Report for Shell-SIEP, Rijswijk, project TN-97-036, 1998, 1-37.
1. E. Pękalska, R.P. W. Duin, M.A. Kraaijveld, and D. de Ridder, An overview of multidimensional scaling techniques with application to Shell data, Report for Shell-SIEP, Rijswijk, project TN-97-036, 1998, 1-54.
Software
R.P.W. Duin, P. Juszczak, P. Paclik, E. Pękalska, D. de Ridder and D.M.J. Tax, PRTools, a Matlab toolbox for pattern recognition, http://prtools.org, 2004.