Convex non-negative matrix factorization in the wild

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 523-532

  Thurau, Christian ^a   Kersting, Kristian ^a   Bauckhage, Christian ^a  

^a FRAUNHOFER IAIS   (Germany)

Author keywords

Archetypal analysis; Data handling; Data mining; Matrix decomposition; Non negative matrix factorization; Social network analysis

Indexed keywords

ARCHETYPAL ANALYSIS; CLUSTER CENTROIDS; CONVEX COMBINATIONS; CONVEX HULL; DATA POINTS; DATA SETS; EMBEDDINGS; EXPERIMENTAL EVALUATION; GAUSSIAN POINT; INPUT MATRICES; LARGE DATASETS; MATRIX DECOMPOSITION; NON-NEGATIVE MATRIX; NONNEGATIVE MATRIX FACTORIZATION; REAL WORLD DATA; RECONSTRUCTION QUALITY; SOCIAL NETWORK ANALYSIS; SOCIAL NETWORKS;

BLIND SOURCE SEPARATION; COMPUTATIONAL GEOMETRY; COMPUTER VISION; ELECTRIC NETWORK ANALYSIS; FACTORIZATION; LIGHT MEASUREMENT; MATRIX ALGEBRA;

DATA HANDLING;

EID: 77951195721     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.55     Document Type: Conference Paper

References (22)

1. I. Jolliffe, Principal Component Analysis. Springer, 1986.
2. G. Golub and J. van Loan, Matrix Computations, 3rd ed. Johns Hopkins University Press, 1996.
3. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-799, 1999.
4. D. Cai, X. He, X. Wu, and J. Han, "Non-negative matrix factorization on manifold," in Proc. IEEE Int. Conf. on Data Mining, 2008.
5. J. Kim and H. Park, "Toward faster nonnegative matrix factorization: A new algorithm and comparisons," in Proc. IEEE Int. Conf. on Data Mining, 2008.
6. S. Suvrit, "Block-iterative algorithms for non-negative matrix approximation," in Proc. IEEE Int. Conf. on Data Mining, 2008.
7. C. H. Ding, T. Li, and M. I. Jordan, "Convex and Semi- Nonnegative Matrix Factorizations," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 99, no. 1, 5555.
8. A. Cutler and L. Breiman, "Archetypal Analysis," Technometrics, vol. 36, no. 4, pp. 338-347, 1994.
9. T. G. Kolda and J. Sun, "Scalable tensor decompositions for multi-aspect data mining," in Proc. IEEE Int. Conf. on Data Mining, 2008.
10. D. Donoho and J. Tanner, "Neighborliness of Randomly- Projected Simplices in High Dimensions," Proc. of the Nat. Academy of Sciences, vol. 102, no. 27, pp. 9452-9457, 2005.
11. P. Hall, J. Marron, and A. Neeman, "Geometric Representation of High Dimension Low Sample Size Data," J. of the Royal Statistical Society B, vol. 67, no. 3, pp. 427-444, 2005.
12. G. Ziegler, Lectures on Polytopes. Springer, 1995.
13. M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf, Computational Geometry. Springer, 2000.
14. C. Faloutsos and K.-I. Lin, "FastMap: A Fast Algorithm for Indexing, Data-mining and Visualization of Traditional and Multimedia Datasets," in Proc. ACM SIGMOD, 1995.
15. I. Hueter, "Limit Theorems for the Convex Hull of Random Points in Higher Dimensions," Trans. of the American Mathematical Society, vol. 351, no. 11, pp. 4337-4363, 1999.
16. A. Torralba, R. Fergus, and W. T. Freeman, "80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1958-1970, 2008.
17. B. Klingenberg, J. Curry, and A. Dougherty, ""non-negative matrix factorization: Ill-posedness and a geometric algorithm"," Pattern Recognition, vol. 42, no. 5, pp. 918 - 928, 2008.
18. J. Aitchison, "The Statistical Analysis of Compositional Data," J. of the Royal Statistical Society B, vol. 44, no. 2, pp. 139-177, 1982.
19. G. Heidemann, "The principal components of natural images revisited," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, 2006.
20. A. Talwalkar, S. Kumar, and H. Rowley, "Large-scale manifold learning," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
21. N. Srebro, J. D. M. Rennie, and T. S. Jaakola, "Maximummargin matrix factorization," in Advances in Neural Information Processing Systems 17. MIT Press, 2005.
22. J. Rennie and N. Srebro, "Fast maximum margin matrix factorization for collaborative prediction," in Proc. Int. Conf. on Machine Learning, 2005.