The dissimilarity space: Bridging structural and statistical pattern recognition

Pattern Recognition Letters

Volumn 33, Issue 7, 2012, Pages 826-832

  Duin, Robert P W ^a   Pçkalska, Elbieta ^b  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Dissimilarity representation; Dissimilarity space; Representation set; Structural pattern recognition; Vector space

Indexed keywords

AUTOMATIC SYSTEMS; DISSIMILARITY REPRESENTATION; DISSIMILARITY SPACE; HISTORICAL REVIEW; HUMAN EXPERT; NATURAL OBJECTS; OBJECT REPRESENTATIONS; PATTERN CLASS; REAL-WORLD DATASETS; REPRESENTATION SET; STATISTICAL APPROACH; STATISTICAL LEARNING; STATISTICAL PATTERN RECOGNITION; STRUCTURAL PATTERN RECOGNITION; TRAINING SETS;

PATTERN RECOGNITION; VECTOR SPACES;

STATISTICS;

EID: 84858439328     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.04.019     Document Type: Conference Paper

References (33)

1. Alimoglu, F.; Alpaydin, E.; 1997. Combining multiple representations and classifiers for pen-based handwritten digit recognitio. In: ICDAR. pp. 637-640.
2. Calana, Y.P.; Reyes, E.B.G.; Orozco-Alzate, M.; Duin, R.P.W.; 2010. Prototype selection for dissimilarity representation by a genetic algorithm. In: ICPR 2010. pp. 177-180.
3. S. Canu, X. Mary, and A. Rakotomamonjy Functional learning through kernel Advances in Learning Theory: Methods Models and Applications, NATO Science Series III: Computer and Systems Sciences vol. 190 2003 IOS Press Amsterdam 89 110
4. Chang, C.-C.; Lin, C.-J.; 2001. LIBSVM: a library for support vector machines. Software available at .
5. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press UK
6. D. Dennett Consciousness Explained 1991 Penguin
7. Duin, R.P.W.; Loog, M.; Pe kalska, E.; Tax, D.M.J.; 2010. Feature-based dissimilarity space classification. In: Unay, D.; Cataltepe, Z.; Aksoy, S. (Eds.), Recognizing Patterns in Signals, Speech, Images, and Videos, ICPR 2010, vol. 6388, Springer, pp. 46-55.
8. S. Edelman Representation and Recognition in Vision 1999 MIT Press Cambridge
9. K. Fu Syntactic Pattern Recognition and Applications 1982 Pretice-Hall
10. L. Goldfarb A new approach to pattern recognition L. Kanal, A. Rosenfeld, Progress in Pattern Recognition vol. 2 1985 Elsevier 241 402
11. Goldfarb, L.; Gay, D.; Golubitsky, O.; Korkin, D.; 2004. What is a structural representation? Tech. rep. URL .
12. B. Haasdonk Feature space interpretation of SVMs with indefinite kernels IEEE TPAMI 25 5 2005 482 492 (Pubitemid 40556105)
13. B. Haasdonk, and H. Burkhardt Invariant kernel functions for pattern analysis and machine learning Machine Learning 68 1 2007 35 61 (Pubitemid 46828727)
14. S. Hochreiter, and K. Obermayer Support vector machines for dyadic data Neural Computation 18 6 2006 1472 1510 (Pubitemid 43778739)
15. D. Jacobs, D. Weinshall, and Y. Gdalyahu Classification with non-metric distances: Image retrieval and class representation IEEE TPAMI 22 6 2000 583 600
16. J. Kruskal Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis Psychometrika 29 1964 1 27
17. G.R.G. Lanckriet, N. Cristianini, P.L. Bartlett, L.E. Ghaoui, and M.I. Jordan Learning the kernel matrix with semidefinite programming J. Mach. Learn. Res. 5 2004 27 72
18. M. Lozano, J.M. Sotoca, J.S. Sánchez, F. Pla, E. Pekalska, and R.P.W. Duin Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces Pattern Recognit. 39 10 2006 1827 1838 (Pubitemid 43947094)
19. V. Mottl, S. Dvoenko, O. Seredin, C. Kulikowski, and I. Muchnik Featureless pattern recognition in an imaginary Hilbert space and its application to protein fold classification P. Perner, Machine Learning and Data Mining in Pattern Recognition LNCS vol. 2123 2001 Springer Berlin/Heidelberg 322 336 (Pubitemid 33312172)
20. Mottl, V.; Seredin, O.; Dvoenko, S.; Kulikowski, C.; Muchnik, I.; 2002. Featureless pattern recognition in an imaginary Hilbert space. In: ICPR 2002. pp. II: 88-91.
21. Ong, C.; Mary, X.; Canu, S.; Smola, S.A.J.; 2004. Learning with non-positive kernels. In: International Conference on Machine Learning. Brisbane, Australia, pp. 639-646. (Pubitemid 40290863)
22. E. Pçkalska, and R.P.W. Duin On combining dissimilarity representations J. Kittler, F. Roli, Multiple Classifier Systems LNCS vol. 2096 2001 Springer-Verlag 359 368 (Pubitemid 33303243)
23. E. Pçkalska, and R.P.W. Duin The Dissimilarity Representation for Pattern Recognition Foundations and Applications 2005 World Scientific Singapore
24. Pçkalska, E.; Duin, R.P.W.; 2006. Dissimilarity-based classification for vectorial representations. In: ICPR2006. pp. III: 137-140.
25. E. Pçkalska, and R.P.W. Duin Beyond traditional kernels: Classification in two dissimilarity-based representation spaces IEEE Trans. Syst. Man. Cybernet.; Part C: Appl. Rev. 38 6 2008 729 744
26. E. Pçkalska, R.P.W. Duin, and P. Paclík Prototype selection for dissimilarity-based classifiers Pattern Recognit. 39 2 2006 189 208
27. E. Pçkalska, and B. Haasdonk Kernel discriminant analysis for positive definite and indefinite kernels IEEE TPAMI 31 6 2009 1017 1032
28. K. Riesen, M. Neuhaus, and H. Bunke Graph embedding in vector spaces by means of prototype selection GbRPR Lecture Notes in Computer Science vol. 4538 2007 Springer 383 393
29. R. Shepard The analysis of proximities: Multidimensional scaling with an unknown distance function. I Psychometrika 27 1962 125 140
30. B. Spillmann, M. Neuhaus, H. Bunke, E. Pekalska, and R.P.W. Duin Transforming strings to vector spaces using prototype selection SSPR/SPR LNCS vol. 4109 2006 Springer 287 296 (Pubitemid 44543117)
31. S. Watanabe Pattern Recognition: Human and Mechanical 1985 Wiley
32. Wilson, C.; Garris, M.; 1992. Handprinted character database 3. Tech. rep.; National Institute of Standards and Technology.
33. R.C. Wilson, and E.R. Hancock Spherical embedding and classification SSPR/SPR LNCS vol. 6218 2010 Springer 589 599