Order-preserving factor analysis-application to longitudinal gene expression

IEEE Transactions on Signal Processing

Volumn 59, Issue 9, 2011, Pages 4447-4458

  Puig, Arnau Tibau ^a,e   Wiesel, Ami ^b   Zaas, Aimee K ^c,d   Woods, Chris W ^d   Ginsburg, Geoffrey S ^c   Fleury, Gilles ^e   Hero, Alfred O ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^c DUKE UNIVERSITY   (United States)

^d DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e SUPELEC   (France)

Author keywords

Dictionary learning; genomic signal processing; misaligned data processing; structured factor analysis

Indexed keywords

DATA HANDLING; GENE EXPRESSION; MULTIVARIANT ANALYSIS; SIGNAL PROCESSING; TIME SERIES ANALYSIS;

DICTIONARY LEARNING; FACTOR ANALYSIS METHOD; GENE EXPRESSION DATA; GENOMIC-SIGNAL-PROCESSING; MULTIVARIATE TIME SERIES; NONCONVEX OPTIMIZATION; ORDER PRESERVING; SPECIFIC ORDERING;

FACTOR ANALYSIS;

EID: 80051707460     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2011.2157146     Document Type: Article

References (31)

1. I. Johnstone and A. Lu, "On consistency and sparsity for principal components analysis in high dimensions," J. Amer. Statist. Assoc., vol. 104, no. 486, pp. 682-693, 2009.
2. R. Jenatton, G. Obozinski, and F. Bach, "Structured sparse principal component analysis," in Proc. 13th Int. Conf. Artif. Intell. Statist. (AISTATS), 2010, vol. 9.
3. T. Blumensath and M. Davies, "Sparse and shift-invariant representations of music," IEEE Trans. Speech Audio Process., vol. 14, no. 1, pp. 50-50, 2006.
4. K. Pearson, "LIII. On lines and planes of closest fit to systems of points in space," Philosoph. Mag. Ser. 6, vol. 2, no. 11, pp. 559-572, 1901.
5. J. Carroll and J. Chang, "Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition," Psychometrika, vol. 35, no. 3, pp. 283-319, 1970.
6. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, 2006. (Pubitemid 44637761)
7. K. Kreutz-Delgado, J. Murray, B. Rao, K. Engan, T. Lee, and T. Sejnowski, "Dictionary learning algorithms for sparse representation," Neural Comput., vol. 15, no. 2, pp. 349-396, 2003. (Pubitemid 37049825)
8. B. Olshausen and D. Field, "Sparse coding with an overcomplete basis set: A strategy employed by V1?," Vision Res., vol. 37, no. 23, pp. 3311-3325, 1997. (Pubitemid 27493805)
9. D.Witten, R. Tibshirani, and T. Hastie, "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis," Biostatistics, vol. 10, no. 3, pp. 515-515, 2009.
10. B. Mailhé, S. Lesage, R. Gribonval, F. Bimbot, and P. Vandergheynst, "Shift-invariant dictionary learning for sparse representations: Extending K-SVD," in Proc. 16th Eur. Signal Process. Conf., 2008, vol. 4.
11. A. Zaas et al., "Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans," Cell Host Microbe, vol. 6, no. 3, pp. 207-217, 2009.
12. N. Sidiropoulos, G. Giannakis, and R. Bro, "Blind PARAFAC receivers for DS-CDMA systems," IEEE Trans. Signal Process., vol. 48, no. 3, pp. 810-823, 2000. (Pubitemid 30576249)
13. D. Lee and H. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999.
14. N. Srebro, J. Rennie, and T. Jaakkola, "Maximum-margin matrix factorization," Adv. Neural Inf. Process. Syst., vol. 17, pp. 1329-1336, 2005.
15. T. Kolda and B. Bader, "Tensor decompositions and applications," SIAM Rev., vol. 51, no. 3, pp. 455-500, 2009.
16. G. Bergqvist and E. Larsson, "The higher-order singular value decomposition: Theory and an application (lecture notes)," IEEE Signal Process. Mag., vol. 27, pp. 151-154, 2010.
17. M. Lewicki and T. Sejnowski, "Coding time-varying signals using sparse, shift-invariant representations," Adv. Neural Inf. Process. Syst., pp. 730-736, 1999.
18. A. T. Puig, A. Wiesel, and A. Hero, Order-Preserving Factor Analysis Univ. Michigan, Ann Arbor, 2010.
19. L. Sacchi, C. Larizza, P. Magni, and R. Bellazzi, "Precedence temporal networks to represent temporal relationships in gene expression data," J. Biomed. Informat., vol. 40, no. 6, pp. 761-774, 2007. (Pubitemid 350088622)
20. A. Aderem and R. Ulevitch, "Toll-like receptors in the induction of the innate immune response," Nature, vol. 406, no. 6797, pp. 782-787, 2000.
21. R. Irizarry, B. Hobbs, F. Collin, Y. Beazer-Barclay, K. Antonellis, U. Scherf, and T. Speed, "Exploration, normalization and summaries of high density oligonucleotide array probe level data," Biostatistics, vol. 4, no. 2, pp. 249-249, 2003.
22. Y. Jia, M. Salzmann, and T. Darrell, Factorized Latent Spaces with Structured Sparsity EECS Dep., Univ. Calif., Berkeley, 2010, Tech. Rep. UCB/EECS-2010-99.
23. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in Proc. IEEE 12th Int. Conf. Comput. Vision 2010, pp. 2272-2279.
24. M.Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. Royal Statist. Soc. Ser. B Statist. Methodol., vol. 68, no. 1, pp. 49-49, 2006.
25. A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Imag. Sci, vol. 2, pp. 183-202, 2009.
26. E. Lawler and D. Wood, "Branch-and-bound methods: A survey," Operat. Res., vol. 14, no. 4, pp. 699-719, 1966.
27. A. Owen and P. Perry, "Bi-cross-validation of the SVD and the nonnegative matrix factorization," Ann. Appl. Statist., vol. 3, no. 2, pp. 564-594, 2009.
28. S.Wold, "Cross-validatory estimation of the number of components in factor and principal components models," Technometrics, vol. 20, no. 4, pp. 397-405, 1978.
29. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer, 2005.
30. B. Bolstad, R. Irizarry, M. Astrand, and T. Speed, "A comparison of normalization methods for high density oligonucleotide array data based on variance and bias," Bioinformatics, vol. 19, no. 2, p. 185, 2003.
31. J. Neter et al., Applied Linear Statistical Models. Chicago, IL: Irwin Burr Ridge, 1996.