Fast multivariate spatio-temporal analysis via low rank tensor learning

Advances in Neural Information Processing Systems

Volumn 4, Issue January, 2014, Pages 3491-3499

  Bahadori, Mohammad Taha ^a   Yu, Rose ^a   Liu, Yan ^a  

^a University of Southern California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE; OPTIMIZATION; TENSORS;

EFFICIENT ANALYSIS; INTER-DEPENDENCES; LEARNING FRAMEWORKS; OPTIMIZATION PROBLEMS; SPATIAL CLUSTERING; SPATIO-TEMPORAL DATA; SPATIOTEMPORAL ANALYSIS; SYNTHETIC DATASETS;

MULTIVARIANT ANALYSIS;

EID: 84937954709     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. T. Anderson. Estimating linear restrictions on regression coefficients for multivariate normal distributions. The Annals of Mathematical Statistics, pages 327-351, 1951.
2. P. Baldi and K. Hornik. Neural networks and principal component analysis: Learning from examples without local minima. Neural networks, 2(1): 53-58, 1989.
3. A. Barron, A. Cohen, W. Dahmen, and R. DeVore. Approximation and learning by greedy algorithms. The Annals of Statistics, 2008.
4. D. Bertsekas and J. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Prentice Hall Inc, 1989.
5. E. Bonilla, K. Chai, and C. Williams. Multi-task Gaussian Process Prediction. In NIPS, 2007.
6. W. Chu, V. Sindhwani, Z. Ghahramani, and S. Keerthi. Relational learning with Gaussian processes. In NIPS, 2006.
7. N. Cressie and H. Huang. Classes of nonseparable, spatio-temporal stationary covariance functions. JASA, 1999.
8. N. Cressie and G. Johannesson. Fixed rank kriging for very large spatial data sets. JRSS B (Statistical Methodology), 70(1): 209-226, 2008.
9. N. Cressie, T. Shi, and E. Kang. Fixed rank filtering for spatio-temporal data. J. Comp. Graph. Stat., 2010.
10. N. Cressie and C. Wikle. Statistics for spatio-temporal data. John Wiley & Sons, 2011.
11. D. Gabay and B. Mercier. A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Computers & Mathematics with Applications, 2(1): 17-40, 1976.
12. S. Gandy, B. Recht, and I. Yamada. Tensor completion and low-n-rank tensor recovery via convex optimization. Inverse Problems, 2011.
13. M. Gu, A. Ruhe, G. Sleijpen, H. van der Vorst, Z. Bai, and R. Li. 5. Generalized Hermitian Eigenvalue Problems. Society for Industrial and Applied Mathematics, 2000.
14. E. Isaaks and R. Srivastava. Applied geostatistics. London: Oxford University, 2011.
15. A. Jalali, S. Sanghavi, C. Ruan, and P. Ravikumar. A dirty model for multi-task learning. In NIPS, 2010.
16. T. Kolda and B. Bader. Tensor decompositions and applications. SIAM review, 2009.
17. W.-J. Li and D.-Y. Yeung. Relation regularized matrix factorization. In IJCAI, 2009.
18. X. Long, L. Jin, and J. Joshi. Exploring trajectory-driven local geographic topics in foursquare. In UbiComp, 2012.
19. A. Lozano, H. Li, A. Niculescu-Mizil, Y. Liu, C. Perlich, J. Hosking, and N. Abe. Spatialtemporal causal modeling for climate change attribution. In KDD, 2009.
20. F. Nie, H. Huang, X. Cai, and C. H. Ding. Efficient and robust feature selection via joint l2,1-norms minimization. In NIPS, 2010.
21. B. Romera-Paredes, H. Aung, N. Bianchi-Berthouze, and M. Pontil. Multilinear multitask learning. In ICML, 2013.
22. S. Shalev-Shwartz, A. Gonen, and O. Shamir. Large-scale convex minimization with a low-rank constraint. In ICML, 2011.
23. S. Shalev-Shwartz, N. Srebro, and T. Zhang. Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. SIAM Journal on Optimization, 2010.
24. R. Tomioka, K. Hayashi, and H. Kashima. Convex Tensor Decomposition via Structured Schatten Norm Regularization. NIPS, 2013.
25. T. Zhang. Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations. IEEE Trans Inf Theory, pages 4689-4708, 2011.
26. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In NIPS, 2003.
27. H. Zhou, L. Li, and H. Zhu. Tensor regression with applications in neuroimaging data analysis. JASA, 2013.
28. J. Zhou, J. Chen, and J. Ye. MALSAR: Multi-tAsk Learning via StructurAl Regularization. http://www.public.asu.edu/~jye02/Software/MALSAR/, 2011.