Relational generative topographic mapping

Neurocomputing

Volumn 74, Issue 9, 2011, Pages 1359-1371

  Gisbrecht, Andrej ^a   Mokbel, Bassam ^a   Hammer, Barbara ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Dissimilarity data; Relational data mining; Topographic mapping

Indexed keywords

DATA POINTS; DATA VECTORS; DISSIMILARITY DATA; EUCLIDEAN; EXPECTATION MAXIMIZATION; GENERATIVE TOPOGRAPHIC MAPPING; HIGH DIMENSIONAL DATA; OUT-OF-SAMPLE EXTENSION; RELATIONAL DATA; RELATIONAL DATA MINING; SPARSE REPRESENTATION; STATISTICAL MODELS; TOPOGRAPHIC MAPPING;

DATA COMPRESSION; DATA MINING; MAPPING; VECTOR SPACES; VECTORS; VISUALIZATION;

DATA VISUALIZATION;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA ANALYSIS; DATA MINING; DATA SYNTHESIS; FEASIBILITY STUDY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RELATIONAL GENERATIVE TOPOGRAPHIC MAPPING; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 79953037235     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.12.011     Document Type: Article

References (30)

1. Barreto G.A., Araujo A.F.R., Kremer S. A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case. Neural Computation 2003, 15(6):1255-1320.
2. Bauer H.-U., Pawelzik K. Quantifying the neighborhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks 1992, 3(4):570-579.
3. Bishop C., Svensen M., Williams C. The generative topographic mapping. Neural Computation 1998, 10(1):215-234.
4. Boulet R., Jouve B., Rossi F., Villa N. Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 2008, 71(7-9):1257-1273.
5. Chen Y., Garcia E.K., Gupta M.R., Rahimi A., Cazzani L. Similarity-based classification: concepts and algorithms. Journal of Machine Learning Research 2009, 10:747-776.
6. Cilibrasi R., Vitanyi M.B. Clustering by compression. IEEE Transactions on Information Theory 2005, 51(4):1523-1545.
7. Graepel T., Obermayer K. A stochastic self-organizing map for proximity data. Neural Computation 1999, 11:139-155.
8. B. Haasdonk, C. Bahlmann, Learning with distance substitution kernels, in: Pattern Recognition-Proceedings of the 26th DAGM Symposium, 2004.
9. Hammer B., Hasenfuss A. Topographic mapping of large dissimilarity data sets. Neural Computation 2010, 22(9):2229-2284.
10. Hammer B., Micheli A., Sperduti A., Strickert M. Recursive self-organizing network models. Neural Networks 2004, 17(8-9):1061-1086.
11. Hasenfuss A., Hammer B. Relational topographic maps. IDA 2007 2007, 93-105. M.R. Berthold, J. Shawe-Taylor, N. Lavrac (Eds.).
12. Hathaway R.J., Bezdek J.C. Nerf c-means: non-Euclidean relational fuzzy clustering. Pattern Recognition 1994, 27(3):429-437.
13. Heskes T. Energy functions for self-organizing maps. Kohonen Maps 1999, 303-315. Elsevier, Amsterdam. E. Oja, S. Kaski (Eds.).
14. Kaski S., Nikkilä J., Oja M., Venna J., Törönen P., Castren E. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics 2003, 4:48.
15. Kaski S., Venna J. Local multidimensional scaling. Neural Networks 2006, 19(6):889-899.
16. Lee J.A., Verleysen M. Quality assessment of dimensionality reduction: rank-based criteria. Neurocomputation 2009, 72(7-9):1431-1443.
17. Venna J., Peltonen J., Nybo K., Aidos H., Kaski S. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research 2010, 11:451-490.
18. A. Gisbrecht, B. Mokbel, B. Hammer, The Nyström approximation for relational generative topographic mappings, in: B. Hammer, F. Sha, L. van der Maaten, A. Smola (Eds.), Workshop Proceedings of the NIPS Workshop on Challenges of Data Visualization, 2010.
19. B. Mokbel, A. Gisbrecht, B. Hammer, On the effect of clustering on quality assessment measures for dimensionality reduction. in: B. Hammer, F. Sha, L. van der Maaten, A. Smola (Eds.), Workshop Proceedings of the NIPS Workshop on Challenges of Data Visualization, 2010.
20. Kohonen T. Self-organizing Maps 1995, Springer.
21. Kohonen T., Somervuo P. How to make large self-organizing maps for nonvectorial data. Neural Networks 2002, 15:945-952.
22. Laub J., Roth V., Buhmann J.M., Müller K.-R. On the information and representation of non-Euclidean pairwise data. Pattern Recognition 2006, 39:1815-1826.
23. van der Maaten L.J.P., Hinton G.E. Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research 2008, 9(November):2579-2605.
24. Olier I., Vellido A., Giraldo J. Kernel generative topographic mapping. ESANN '2010 2010, 481-486. M. Verleysen (Ed.).
25. Pekalska E., Duin B. The Dissimilarity Representation for Pattern Recognition 2005, World Scientific.
26. Roth V., Laub J., Kawanabe M., Buhmann J.M. Optimal cluster preserving embedding of nonmetric proximity data. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(12):1540-1551.
27. Mevissen H., Vingron M. Quantifying the local reliability of a sequence alignment. Protein Engineering 1996, 9:127-132.
28. Mokbel B., Hasenfuss A., Hammer B. Graph-based representation of symbolic musical data. GbRPR 2009 2009, 42-51. Springer. A. Torsello, F. Escolano, L. Brun (Eds.).
29. Tino P., Kaban A., Sun Y. A generative probabilistic approach to visualizing sets of symbolic sequences. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD-2004 2004, 701-706. ACM Press. R. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh (Eds.).
30. Yin H. On the equivalence between kernel self-organising maps and self-organising mixture density network. Neural Networks 2006, 19(6):780-784.