Correlating shape and functional properties using decomposition approaches

Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

Volumn , Issue , 2010, Pages 398-403

  Dornbusch, Daniel ^a   Haschke, Robert ^a   Menzel, Stefan ^b   Wersing, Heiko ^b  

^a BIELEFELD UNIVERSITY   (Germany)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DECOMPOSITION ALGORITHM; DECOMPOSITION APPROACH; FUNCTIONAL PROPERTIES; K-MEANS; LOCAL CORRELATIONS; LOCAL SHAPE; MULTI-MODAL DATA; NONNEGATIVE MATRIX FACTORIZATION; TEST SCENARIO; TURBINE BLADE;

ARTIFICIAL INTELLIGENCE; BLIND SOURCE SEPARATION; FACTORIZATION; MATRIX ALGEBRA; TURBOMACHINE BLADES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77957864684     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Arima, T.; Sonoda, T.; Shirotori, M.; Tamura, A.; and Kikuchi, K. 1999. A numerical investigation of transonic axial compressor rotor flow using a low-reynolds-number k-e turbulence model. ASME Journal of Turbomachinery 121(1):44-58. (Pubitemid 29306135)
2. Arthur, D., and Vassilvitskii, S. 2007. k-Means++ The advantages of careful seeding. In Proc. Symposium on Discrete Algorithms (SODA -07), 1027-1035.
3. Bartlett, M. S. 1998. Face image analysis by unsupervised learning and redundancy reduction. Ph.D. Dissertation, University of California, San Diego.
4. Bozakov, Z.; Graening, L.; Hasler, S.; Wersing, H.; and Menzel, S. 2008. Unsupervised extraction of design components for a 3d parts-based representation. In Proc. IEEE IJCNN '08, 2009-2016.
5. Ding, C.; He, X.; and Simon, H. D. 2005. On the equivalence of nonnegative matrix factorization and spectral clustering. In Proc. SIAM Int. Conf. on Data Mining (SDM '05), 606-610.
6. Ding, C.; Li, T.; and Jordan, M. I. 2006. Convex and seminonnegative matrix factorizations. Technical report, Lawrence Berkeley National Labratory.
7. Ding, C. 2006. Orthogonal nonnegative matrix tri-factorizations for clustering. In Proc. Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), 126-135. (Pubitemid 44535510)
8. Eggert, J., and Körner, E. 2004. Sparse coding and NMF. In Proc. IEEE Int. Joint Conf. on Neural Networks, 2529-2533.
9. Fisher, R. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7:179-188.
10. Hasler, S.; Wersing, H.; and Körner, E. 2007. Combining reconstruction and discrimination with class-specific sparse coding. Neural Computation 19(7): 1897-1918.
11. Hotelling, H. 2009. The most predictable criterion. Journal of Educational Psychology 26:139-142.
12. Hoyer, P. O. 2004. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research (JMLR '04)5:1457-1469.
13. Inaba, M.; Katoh, N.; and Imai, H. 1994. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering. In Proc. 10th ACM Symposium on Computational Geometry, 332-339.
14. Lee, D. D., and Seung, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788-791. (Pubitemid 129515838)
15. Lee, D. D., and Seung, H. S. 2000. Algorithms for non-negative matrix factorization. In Proc. Int. Conf. on Neural Information Processing Systems (NIPS), 556-562.
16. Li, T., and Ding, C. 2006. The relationships among various nonnegative matrix factorization methods for clustering. In Proc. 6th Int. Conf. on Data Mining (ICDM '06), 362-371. (Pubitemid 47485816)
17. Li, S. Z.; Hou, X. W.; Zhang, H. J.; and Cheng, Q. S. 2001. Learning spatially localized, parts-based representation. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, 207-212.
18. MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations. In Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability (BSMSP '67), 281-297. University of California Press.
19. Menzel, S., and Sendhoff, B. 2008. Representing the change - free form deformation for evolutionary design optimisation. In Yu, T.; Davis, D.; Baydar, C.; and Roy, R., eds., Evolutionary Computation in Practice. Springer, chapter 4, 63-86.
20. Osowski, S.; Majkowski, A.; and Cichocki, A. 1997. Robust PCA neural networks for random noise reduction of the data. In Proc. of the 1997 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP '97), 3397-3400. Washington, DC, USA: IEEE Computer Society.
21. Pascual-Montano, A.; Carazo, J. M.; Kochi, K.; Lehmann, D.; and Pascual-Marqui, R. D. 2006. Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 28(3):403-415.
22. Pearson, K. 1901. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2(6):559-572.
23. Shapiro, L. G., and Stockman, G. C. 2001. Computer Vision. Prentice Hall.
24. Turk, M., and Pentland, A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience (JCN '91) 3(1):71-86.
25. van Benthem, M. H., and Keenan, M. R. 2004. Fast algorithm for the solution of large-scale non-negativity-constrained least squares problems. Journal of Osmometries 18(10):441-450.
26. Wall, M. E.; Rechtsteiner, A.; and Rocha, L. M. 2003. Singular value decomposition and principal component analysis. In A Practical Approach to Microarray Data Analysis. Kluwer. chapter 5, 91-109.