Dimensionality reduction via graph structure learning

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2015-August, Issue , 2015, Pages 765-774

  Mao, Qi ^a   Wang, Li ^b   Goodison, Steve ^c   Sun, Yijun ^d  

^a UNIVERSITY AT BUFFALO   (United States)

^b UNIVERSITY OF VICTORIA   (Canada)

^c MAYO CLINIC   (United States)

^d UNIVERSITY AT BUFFALO   (United States)

Author keywords

Clustering; Dimensionality reduction; Graph structure learning; Unsupervised learning

Indexed keywords

DATA REDUCTION; DATA VISUALIZATION; GRAPHIC METHODS; REDUCTION; UNSUPERVISED LEARNING;

CLUSTERING; DIMENSIONALITY REDUCTION; DISCRIMINATIVE FEATURES; GEOMETRIC INFORMATION; GRAPH STRUCTURES; HIGH DIMENSIONAL SPACES; LOW-DIMENSIONAL SPACES; TRADITIONAL CLUSTERING;

DATA MINING;

EID: 84954123824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2783309     Document Type: Conference Paper

References (34)

1. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. JMLR, 6:1817-1853, 2005.
2. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, 2001.
3. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? In Database Theory-ICDT'99, 1999.
4. C. M. Bishop, M. Svensén, and C. K. I. Williams. GTM: the generative topographic mapping. Neural Comput, 10(1):215-234, 1998.
5. S. Boyd and L. Vandenberghe. Conex Optimization. Cambridge University Press, 2004.
6. C. J. C. Burges. Dimension reduction: a guided tour. FTML, 2(4):275-365, 2009.
7. L. Cayton. Algorithms for manifold learning. Technical report, UCSD, 2005.
8. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE T-PAMI, 17(8):790-799, 1995.
9. M. Cheung. Minimum-cost spanning trees. http://people.orie.cornell.edu/dpw/orie6300/fall2008/Recitations/rec09.pdf.
10. C. Curtis, S. P. Shah, S. Chin, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 486(7403):346-352, 2012.
11. C. Ding and X. He. K-means clustering via principal component analysis. In ICML, 2004.
12. E. Erwin, K. Obermayer, and K. Schulten. Self-organizing maps: ordering, convergence properties and energy functions. Biol Cybern, 67:47-55, 1992.
13. M. Filippone, F. Camastra, F. Masulli, and S. Rovetta. A survey of kernel and spectral methods for clustering. Pattern Recogn, 41:176-190, 2008.
14. A. Gorban, B. Kégl, D. Wunsch, and A. Zinovyev. Principal Manifolds for Data Visualisation and Dimension Reduction, Volume 58. Springer, 2007.
15. M. Greaves and C. C. Maley. Clonal evolution in cancer. Nature, 481(7381):306-313, 2012.
16. T. Hastie and W. Stuetzle. Principal curves. JASA, 84:502-516, 1989.
17. R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, 2012.
18. J. T. Jolliffe. Principal Component Analysis. Springer-Verlag, Berlin, 1986.
19. B. Kégl and A. Kryzak. Piecewise linear skeletonization using principal curves. IEEE T-PAMI, 24(1):59-74, 2002.
20. T. Kohonen. Self-organizing Maps. Springer, 1997.
21. Q. Mao, I. Tsang, S. Gao, and L. Wang. Generalized multiple kernel learning with data-dependent priors. IEEE T-NNLS, 29(6):1134-1148, 2015.
22. Q. Mao, L. Yang, L. Wang, S. Goodison, and Y. Sun. SimplePPT: A simple principal tree algorithm. In SDM, 2015.
23. J. Parker, M. Mullins, M. Cheang, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol, 27(8):1160-1167, 2009.
24. M. Radovanović, A. Nanopoulos, and M. Ivanović. Hubs in space: popular nearest neighbors in high-dimensional data. JMLR, 11:2487-2531, 2010.
25. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
26. L. Saul and S. Roweis. Think globally, fit locally: unsupervised learning of low dimensional mainfolds. JMLR, 4:119-155, 2003.
27. B. Schölkopf, A. Smola, and K. Muller. Kernel principal component analysis. Advances in Kernel Methods - Support Vector Learning, pages 327-352, 1999.
28. A. J. Smola, S. Mika, B. Schölkopf, and R. C. Williamson. Regularized principal manifolds. JMLR, 1:179-209, 2001.
29. L. Song, A. Smola, A. Gretton, and K. Borgwardt. A dependence maximization view of clustering. In ICML, 2007.
30. Y. Sun, J. Yao, N. Nowak, and S. Goodison. Cancer progression modeling using static sample data. Genome Biol, 15(8):440, 2014.
31. J. B. Tenenbaum, V. deSilva, and J. C. Landford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, 2000.
32. R. Tibshirani. Principal curves revisited. Stat Comput, 2:183-190, 1992.
33. K. Q. Weinberger and L. K. Saul. An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In AAAI, 2006.
34. J. Yao, Q. Mao, S. Goodison, V. Mai, and Y. Sun. Feature selection for unsupervised learning through local learning. Pattern Recogn Lett, 53:100-107, 2015.