Latent smooth skeleton embedding

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Volumn , Issue , 2017, Pages 2703-2709

  Wang, Li ^a   Mao, Qi ^b   Tsang, Ivor W ^c  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b HERE Company   (Australia)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; MATRIX ALGEBRA;

COMPUTATIONAL BIOLOGY; HIGH DIMENSIONAL DATA; LOW-DIMENSIONAL SPACES; MAXIMUM A POSTERIORI ESTIMATION; NEIGHBORHOOD GRAPHS; POWER LAW DISTRIBUTION; SKELETON STRUCTURE; STRUCTURED LEARNING;

MUSCULOSKELETAL SYSTEM;

EID: 85030481919     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Belkin, M., and Niyogi, P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, Volume 14, 585-591.
2. Boyd, S.; Parikh, N. and Eric, C.; Peleato, B.; and Eckstein, J. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. FTML 3(1):1-122.
3. Burges, C. J. C. 2009. Dimension reduction: a guided tour. FTML 2(4):275-365.
4. Byrd, R. H.; Lu, P.; Nocedal, J.; and Zhu, C. 1995. A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5):1190-1208.
5. Curtis, C.; Shah, S. P.; Chin, S.; et al. 2012. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403):346-352.
6. Dudík, M.; Phillips, S. J.; and Schapire, R. E. 2007. Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. JMLR 8(6).
7. Elhamifar, E., and Vidal, R. 2011. Sparse manifold clustering and embedding. In NIPS, 55-63.
8. Greaves, M., and Maley, C. C. 2012. Clonal evolution in cancer. Nature 481(7381):306-313.
9. Gupta, A. K., and Nagar, D. K. 1999. Matrix variate distributions, Volume 104. CRC Press.
10. Lake, B., and Tenenbaum, J. 2010. Discovering structure by learning sparse graph. In Proceedings of the 33rd Annual Cognitive Science Conference.
11. Lawrence, N. D. 2005. Probabilistic non-linear principal component analysis with gaussian process latent variable models. JMLR 6:1783-1816.
12. Lawrence, N. D. 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models. JMLR 13(1):1609-1638.
13. Liu, Q., and Ihler, A. T. 2011. Learning scale free networks by reweighted l1 regularization. In AISTATS, 40-48.
14. Mao, Q., and Tsang, I. W. 2010. Parameter-free spectral kernel learning. UAI.
15. Mao, Q.; Yang, L.; Wang, L.; Goodison, S.; and Sun, Y. 2015. SimplePPT: A simple principal tree algorithm. In SDM.
16. Mao, Q.; Wang, L.; and Tsang, I. W. 2016. A unified probabilistic framework for robust manifold learning and embedding. Machine Learning.
17. Nie, F.; Xu, D.; Tsang, I. W.; and Zhang, C. 2009. Spectral embedded clustering. In IJCAI, 1181-1186.
18. Saul, L. K., and Roweis, S. T. 2003. Think globally, fit locally: unsupervised learning of low dimensional manifolds. JMLR 4:119-155.
19. Schmidt, M. 2010. Graphical model structure learning with l1-regularization. Ph.D. Dissertation, University of British Columbia, Vancouver.
20. Scholkopf, B., and Smola, A. 2001. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
21. Schölkopf, B.; Smola, A.; and Muller, K. 1999. Kernel principal component analysis. Advances in Kernel Methods - Support Vector Learning 327-352.
22. Smola, A. J., and Kondor, R. 2003. Kernels and regularization on graphs. In COLT. 144-158.
23. Van der Maaten, L.; Postma, E. O.; and van den Herik, H. J. 2009. Dimensionality reduction: A comparative review. Tilburg University Technical Report, TiCC-TR 2009-005.
24. Weinberger, K. Q., and Saul, L. K. 2006. Unsupervised learning of image manifolds by semidefinite programming. IJCV 70(1):77-90.
25. Weinberger, K.; Packer, B.; and Saul, L. 2005. Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization. In AISTATS, 381-388.
26. Weinberger, K.; Sha, F.; and Saul, L. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In ICML, 106.
27. Xiao, L.; Sun, J.; and Boyd, S. 2006. A duality view of spectral methods for dimensionality reduction. In ICML, 1041-1048. ACM.