Sparse nonnegative matrix factorization for classification of gene expression data

2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

Volumn , Issue , 2007, Pages 180-183

  Liu, Weixiang ^a   Yuan, Kehong ^a   Xie, Zhenhua ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Classification; Gene expression data; L p norm; Mixture model; Nonegative matrix factorization; Sparseness

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; FEATURE EXTRACTION; MATHEMATICAL MODELS; MATRIX ALGEBRA;

CLASSIFICATION ACCURACY; DATA FEATURES; GENE EXPRESSION DATA; MIXTURE MODELS; NONEGATIVE MATRIX FACTORIZATION;

GENE EXPRESSION;

EID: 35348844101     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICBBE.2007.49     Document Type: Conference Paper

References (20)

1. P. Baldi, S. Brunak, Bioinformatics: the Machine Learning Approach, 2nd Edition, MIT Press, 2001.
2. V. Vapnik, Statistical Learning Theory, John Wiley and Sons, 1998.
3. L. Saul, D. Lee, "Multiplicative Updates for Classification by Mixture Models", In: T. Dietterich, S. Becker, Z. Ghahramani, eds, Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press, 2002, pp. 897-904.
4. D. Lee, H. Seung, "Learning the parts of objects with nonnegative matrix factorization", Nature, 1999,401, pp. 788-791.
5. W.X. Liu, N.N. Zheng, "Learning sparse features for classification by mixture models", Pattern Recognition Letters, 2004, 25(2):155-161.
6. G. McLachlan, D. Peel, Finite mixture models, John Wiley and sons, 2000.
7. Y. Rubinstein, T. Hastie, "Discriminative vs informative learning", In: D. Heckerman, H. Mannila, D. Pregibon, et al, eds, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA, 1997, pp. 49-53.
8. T. Jaakkola, D. Haussler, "Exploiting generative models in discriminative classifiers", In: M. Kearns, S. Solla, D. Conn, eds, Advances in Neural Information Processing Systems 11. Cambridge, MA, MIT Press, 1999, pp. 487493.
9. W.X. Liu, N.N. Zheng, Q.B. You, "Nonnegative Matrix Factorization and its applications in pattern recognition", Chinese Science Bulletin, 2006, 51(17-18):7-18.
10. M. Chu, R. Plemmons, "Nonnegative (Non-negative) Matrix Factorization and Applications", IMAGE, Bulletin of the International Linear Algebra Society, 2005, 34:2-7.
11. W.X. Liu, K.H. Yuan, "Sparse p-norm Nonnegative Matrix Factorization for Clustering Gene Expression Data", 2006. Submitted.
12. T. Golub, D. Slonim, P. Tamayo, et al, "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring", Science, 1999, 286:531-537.
13. S. Pomeroy, P. Tamayo, M. Gaasenbeek, et al, "Prediction of central nervous system embryonal tumour outcome based on gene expression", Nature, 2002, 415:436-342.
14. A. Bhattacherjee, W. Richards, J. Staunton, et al, "Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses", Proc. Natl Acad. Sci. USA, 2001, 98:13790-13795.
15. Singh D, Febbo P, Ross K, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 2002, 203-209.
16. Statnikov A, Aliferis, C F, Tsamardinos, et al. A Comprehensive Evaluation of Multi category Classification Methods for Microarray Gene Expression Cancer Diagnosis. Bioinformatics, 2005, 21(5): 631-643.
17. O. Okun, H. Priisalu, "Fast Nonnegative Matrix Factorization and Its Application for Protein Fold Recognition", EURASIP Journal on Applied Signal Processing, 2006, Article ID 71817, 8 pages.
18. J. Brunet, P. Tamayo, T. Golub, et al, "Metagenes and molecular pattern discovery using matrix factorization", Proc. Natl. Acad. Sci. USA, 2004, 101(12): 4164-4169.
19. Y. Gao, G. Church, "Improving molecular cancer class discovery through sparse non-negative matrix factorization", Bioinformatics, 2005, 21:3970-3975.
20. S.Z. Li, X.W. Hou, H.J. Zhang, et al, "Learning spatially localized, parts-based representation", In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. 207-212.