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Erschienen in:
Efficiency and Effectiveness Metrics in Evolutionary Algorithms and Their Application Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes

verfasst von : Chun-Xia Ma, Ying-Lian Gao, Dong Wang, Jian Liu, Jin-Xing Liu

Erschienen in: Intelligent Computing Theories and Methodologies

Verlag: Springer International Publishing

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Abstract

Non-negative Matrix Factorization (NMF) has been widely concerned in computer vision and data representation. However, the penalized and restriction L0-norm measure are imposed on the NMF model in traditional NMF methods. In this paper, we propose a novel graph regularized non-negative matrix with L0-constraints (GL0NMF) method which comprises the geometrical structure and a more interpretation sparseness measure. In order to extract the characteristic gene effectively, the steps are shown as follows. Firstly, the original data \( {\mathbf{Q}} \) is decomposed into two non-negative matrices \( {\mathbf{F}} \) and \( {\mathbf{P}} \) by utilizing GL0NMF method. Secondly, characteristic genes are extracted by the sparse matrix \( {\mathbf{F}} \). Finally, the extracted characteristic genes are validated by using Gene Ontology. In conclusion, the results demonstrate that our method can extract more genes than other conventional gene selection methods.

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Vorheriges Kapitel Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection
Nächstes Kapitel Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders
Literatur
1.
Alter, O., Brown, P.O., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. 97, 10101–10106 (2000)CrossRef
2.
Journée, M., Nesterov, Y., Richtárik, P., Sepulchre, R.: Generalized power method for sparse principal component analysis. J. Mach. Learn. Res. 11, 517–553 (2010)MathSciNet
3.
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13, 1450–1464 (2002)CrossRef
4.
Huang, D.-S., Zheng, C.-H.: Independent component analysis-based penalized discriminant method for tumor classification using gene expression data. Bioinformatics 22, 1855–1862 (2006)CrossRef
5.
Witten, D.M., Tibshirani, R., Hastie, T.: A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009)CrossRef
6.
Liu, J.-X., Zheng, C.-H., Xu, Y.: Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition. Comput. Biol. Med. 42, 582–589 (2012)CrossRef
7.
Liu, J., Zheng, C., Xu, Y.: Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses. In: 2011 Fourth International Workshop on Advanced Computational Intelligence (IWACI), pp. 461–464 (2011)
8.
Liu, J.-X., Xu, Y., Zheng, C.-H., Wang, Y., Yang, J.-Y.: Characteristic gene selection via weighting principal components by singular values. PLoS ONE 7, e38873 (2012)CrossRef
9.
Liu, J.-X., Wang, Y.-T., Zheng, C.-H., Sha, W., Mi, J.-X., Xu, Y.: Robust PCA based method for discovering differentially expressed genes. BMC Bioinformatics 14, 1–10 (2013)CrossRef
10.
Aradhya, V.N.M., Masulli, F., Rovetta, S.: A novel approach for biclustering gene expression data using modular singular value decomposition. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds.) CIBB 2009. LNCS, vol. 6160, pp. 254–265. Springer, Heidelberg (2010)CrossRef
11.
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)CrossRef
12.
Li, Y., Ngom, A.: The non-negative matrix factorization toolbox for biological data mining. Source Code Biol. Med. 8, 1–15 (2013)CrossRef
13.
Yang, S., Ye, M.: Global minima analysis of Lee and Seung’s NMF algorithms. Neural Process. Lett. 38, 29–51 (2013)CrossRefMATH
14.
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2000)
15.
Gao, Y., Church, G.: Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics 21, 3970–3975 (2005)CrossRef
16.
Wang, Y., Jia, Y.: Fisher non-negative matrix factorization for learning local features. In: Proceedings of Asian Conference on Computer Vision. Citeseer (2004)
17.
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)MathSciNet
18.
Deng, C., Xiaofei, H., Jiawei, H., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011)CrossRef
19.
Zheng, C.-H., Ng, T.-Y., Zhang, D., Shiu, C.-K., Wang, H.-Q.: Tumor classification based on non-negative matrix factorization using gene expression data. IEEE Trans. Nanobiosci. 10, 86–93 (2011)CrossRef
20.
Du, J.-X., Zhai, C.-M., Ye, Y.-Q.: Face aging simulation based on NMF algorithm with sparseness constraints. In: Huang, D.-S., Gan, Y., Gupta, P., Gromiha, M.M. (eds.) ICIC 2011. LNCS, vol. 6839, pp. 516–522. Springer, Heidelberg (2012)
21.
Kong, X., Zheng, C.-H., Wu, Y., Shang, L.: Molecular cancer class discovery using non-negative matrix factorization with sparseness constraint. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 792–802. Springer, Heidelberg (2007)
22.
Tang, Z., Ding, S.: Nonnegative dictionary learning by nonnegative matrix factorization with a sparsity constraint. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 92–101. Springer, Heidelberg (2012)CrossRef
23.
Peharz, R., Stark, M., Pernkopf, F.: Sparse nonnegative matrix factorization with ℓ0-constraints. Neurocomputing 80, 38–46 (2010)CrossRef
24.
Morup, M., Madsen, K.H., Hansen, L.K.: Approximate L0 constrained non-negative matrix and tensor factorization. In: IEEE International Symposium on Circuits and Systems, ISCAS 2008, pp. 1328–1331 (2008)
25.
Long, X., Lu, H., Peng, Y., Li, W.: Graph regularized discriminative non-negative matrix factorization for face recognition. Multimedia Tools Appl. 72, 2679–2699 (2014)CrossRef
26.
Shen, H., Huang, J.Z.: Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 99, 1015–1034 (2008)MathSciNetCrossRefMATH
27.
Craigon, D.J., James, N., Okyere, J., Higgins, J., Jotham, J., May, S.: NASCArrays: a repository for microarray data generated by NASC’s transcriptomics service. Nucleic Acids Res. 32, D575–D577 (2004)CrossRef
28.
Wu, Z., Irizarry, R.A., Gentleman, R., Murillo, F.M., Spencer, F.: A model based background adjustment for oligonucleotide expression arrays (2004)
29.
Consortium, G.O.: The gene ontology in 2010: extensions and refinements. Nucleic Acids Res. 38, D331–D335 (2010)CrossRefMATH
Metadaten
Titel
Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes
verfasst von
Chun-Xia Ma
Ying-Lian Gao
Dong Wang
Jian Liu
Jin-Xing Liu
Copyright-Jahr
2015
Buch
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22185-4

Electronic ISBN: 978-3-319-22186-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-22186-1_61