Heterogeneous data fusion via space alignment using nonmetric multidimensional scaling

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 177-188

  Choo, Jaegul ^a   Bohn, Shawn ^b   Nakamura, Grant C ^b   White, Amanda M ^b   Park, Haesun ^a  

^a Georgia Institute of Technology   (United States)

^b PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA FUSION; DEFORMATION; EMBEDDINGS; SEMANTICS;

CORRESPONDENCE DATA; GRAPH EMBEDDINGS; HETEROGENEOUS DATA; LAPLACIAN EIGENMAPS; MULTIPLE DATA SETS; NON-METRIC MULTIDIMENSIONAL SCALING; PROCRUSTES ANALYSIS; TENSOR DECOMPOSITION;

DATA MINING;

EID: 84875848281     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.16     Document Type: Conference Paper

References (34)

1. A. Asuncion and D. Newman. UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences, 2007.
2. L. Ballesteros and W. B. Croft. Dictionary methods for cross-lingual information retrieval. In Proceedings of the 7th International Conference on Database and Expert Systems Applications, DEXA '96, pages 791- 801, London, UK, 1996. Springer-Verlag.
3. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003.
4. I. Borg and P. Groenen. Modern multidimensional scaling: Theory and applications. Springer Verlag, 2005.
5. P. A. Chew, B. W. Bader, T. G. Kolda, and A. Abdelali. Cross-language information retrieval using parafac2. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 143-152, New York, NY, USA, 2007. ACM.
6. J. Choo, S. Bohn, and H. Park. Two-stage framework for visualization of clustered high dimensional data. In IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009., pages 67 -74, 2009.
7. R. R. Coifman and S. Lafon. Diffusion maps. Applied and Computational Harmonic Analysis, 21(1):5 - 30, 2006.
8. T. F. Cox and M. A. A. Cox. Multidimensional Scaling. Chapman & Hall/CRC, London, 2000.
9. J. De Leeuw. Applications of convex analysis to multidimensional scaling. Recent developments in statistics, pages 133-146, 1977.
10. E. R. Gansner, Y. Koren, and S. North. Graph drawing by stress majorization. In Graph Drawing, volume 3383 of Lecture Notes in Computer Science, pages 239-250. Springer Berlin/Heidelberg, 2005.
11. C. Goodall. Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society. Series B (Methodological), 53(2):285-339, 1991.
12. J. C. Gower and G. B. Dijksterhuis. Procrustes problems. Oxford University Press, USA, 2004.
13. J. Ham, D. Lee, and L. Saul. Semisupervised alignment of manifolds. In Proceddings of the International Workshop on Artifcial Intelligence and Statistics, pages 120-127, 2005.
14. R. J. Hanson and M. J. Norris. Analysis of measurements based on the singular value decomposition. SIAM Journal on Scientific and Statistical Computing, 2(3):363-373, 1981.
15. D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12):2639-2664, 2004.
16. D. Harman. Overview of the first trec conference. In Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '93, pages 36-47, New York, NY, USA, 1993. ACM.
17. R. Harshman. PARAFAC2: Mathematical and Technical Notes. UCLA working papers in phonetics, page 30, 1972.
18. D. L. Hill, P. G. Batchelor, M. Holden, and D. J. Hawkes. Medical image registration. Physics in medicine and biology, 46:R1-R45, 2001.
19. J. R. Hurley and R. B. Cattell. The Procrustes program: Producing direct rotation to test a hypothesized factor structure. Behavioral Science, 7(2):258-262, 1962.
20. T. Kamada and S. Kawai. An algorithm for drawing general undirected graphs. Information Processing Letters, 31:7-15, April 1989.
21. J. Kruskal. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29:1-27, 1964.
22. J. Kruskal. Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29:115-129, 1964.
23. S. Lafon, Y. Keller, and R. R. Coifman. Data fusion and multicue data matching by diffusion maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:1784-1797, 2006.
24. D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka. Training algorithms for linear text classifiers. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '96, pages 298-306, New York, NY, USA, 1996. ACM.
25. K. A. Pennock and N. E. Miller. System and method for interpreting document contents, U.S. Patent No. 6, 772, 170, August 2004.
26. J. Sammon, John W. A nonlinear mapping for data structure analysis. Computers, IEEE Transactions on, C-18(5):401 - 409, may. 1969.
27. X. Shi, Q. Liu, W. Fan, P. Yu, and R. Zhu. Transfer learning on heterogenous feature spaces via spectral transformation. In Data Mining (ICDM), 2010 IEEE 10th International Conference on, pages 1049 -1054, dec. 2010.
28. J. B. Tenenbaum, V. d. Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290(5500):2319-2323, 2000.
29. W. Torgerson. Multidimensional scaling: I. theory and method. Psychometrika, 17:401-419, 1952.
30. C. Wang and S. Mahadevan. Manifold alignment using procrustes analysis. pages 1120-1127, 2008.
31. C. Wang and S. Mahadevan. A General Framework for Manifold Alignment. In AAAI Fall Symposium on Manifold Learning and its Applications, 2009.
32. C. Wang and S. Mahadevan. Manifold alignment without correspondence. In IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence, pages 1273-1278, 2009.
33. K. Weinberger and L. Saul. Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision, 70:77-90, 2006.
34. L. Xiong, F. Wang, and C. Zhang. Semi-definite manifold alignment. Machine Learning: ECML 2007, 4701:773-781, 2007.