Variable selection based on locally linear embedding mapping for near-infrared spectral analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 131, Issue , 2014, Pages 31-36

  Shan, Ruifeng ^a   Cai, Wensheng ^a   Shao, Xueguang ^a  

^a NANKAI UNIVERSITY   (China)

Author keywords

Forward stepwise selection; Locally linear embedding; Monte Carlo cross validation; Near infrared spectroscopy; Partial least squares regression; Variable selection

Indexed keywords

ANALYTIC METHOD; ARTICLE; EMBEDDING; LOCALLY LINEAR EMBEDDING; MAIZE; MATHEMATICAL VARIABLE; NEAR INFRARED SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; SAMPLE SIZE; SPECTRUM; TABLET; TOBACCO; VALIDATION STUDY;

EID: 84891647183     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.12.002     Document Type: Article

References (34)

1. Hasegawa K., Miyashita Y., Funatsu K. GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. J. Chem. Inf. Comput. Sci. 1997, 37:306-310.
2. Leardi R., Gonzalez A.L. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom. Intell. Lab. Syst. 1998, 41:195-207.
3. Leardi R., Seasholtz M., Pell R. Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data. Anal. Chim. Acta. 2002, 461:189-200.
4. Shen Q., Jiang J.H., Jiao C.X., Huan S.Y., Shen G.L., Yu R.Q. Optimized partition of minimum spanning Tree for piecewise modeling by particle swarm algorithm. QSAR Studies of antagonism of angiotensin II antagonists. J. Chem. Inf. Model. 2004, 44:2027-2031.
5. Lindgren F., Geladi P., Rannar S., Wold S. Interactive variable selection (IVS) for PLS. Part 1: theory and algorithms. J. Chemom. 1994, 8:349-363.
6. Lindgren F., Geladi P., Berglund A., Sjostrom M., Wold S. Interactive variable selection (IVS) for PLS. Part II: chemical applications. J. Chemom. 1995, 9:331-342.
7. Centner V., Massart D.L., de Noord O.E., de Jong S., Vandeginste B.M., Sterna C. Elimination of uninformative variables for multivariate calibration. Anal. Chem. 1996, 68:3851-3858.
8. Ye S.F., Wang D., Min S.G. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemom. Intell. Lab. Syst. 2008, 91:194-199.
9. Cai W.S., Li Y.K., Shao X.G. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemom. Intell. Lab. Syst. 2008, 90:188-194.
10. Xu H., Liu Z.C., Cai W.S., Shao X.G. A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemom. Intell. Lab. Syst. 2009, 97:189-193.
11. Chen T., Martin E. Bayesian linear regression and variable selection for spectroscopic calibration. Anal. Chim. Acta. 2009, 631:13-21.
12. Araujo M.C.U., Saldanha T.C.B., Galvao R.K.H., Yoneyama T., Chame H.C., Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 2001, 57:65-73.
13. Galvao R.K.H., Araujo M.C.U., Fragoso W.D., Silva E.C., Jose G.E., Soares S.F.C., Paiva H.M. A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemom. Intell. Lab. Syst. 2008, 92:83-91.
14. Li H.D., Liang Y.Z., Xu Q.S., Cao D.S. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta. 2009, 648:77-84.
15. Zheng K.Y., Li Q.Q., Wang J.Z., Geng J.P., Cao P., Sui T., Wang X., Du Y.P. Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemom. Intell. Lab. Syst. 2012, 112:48-54.
16. Shao X.G., Zhang M., Cai W.S. Multivariate calibration of near-infrared spectra by using influential variables. Anal. Methods 2012, 4:467-473.
17. Shao X.G., Du G.R., Jing M., Cai W.S. Application of Latent projective graph in variable selection for near infrared spectral analysis. Chemom. Intell. Lab. Syst. 2012, 114:44-49.
18. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
19. Tenenbaum J.B., de Silva V., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
20. Donoho D.L., Grimes C. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. U. S. A. 2003, 100:5591-5596.
21. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 2003, 15:1373-1396.
22. Cleveland W.S., Devlin S.J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 1988, 83:596-610.
23. Saul L.K., Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 2003, 4:119-155.
24. Qi N., Zhang Z.Y., Xiang Y.H., Harrington P.D.B. Locally linear embedding method for dimensionality reduction of tissue sections of endometrial carcinoma by near infrared spectroscopy. Anal. Chim. Acta. 2012, 724:12-19.
25. Jacques J., Bouveyron C., Girard S., Devos O., Duponchel L., Ruckebusch C. Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data. J. Chemom. 2010, 24:719-727.
26. Pillati M., Viroli C. Supervised locally linear embedding for classification: an application to gene expression data analysis. Proceedings of 29th Annual Conference of the German Classification Society (GfKI 2005) 2005, 15-18.
27. Li B., Zheng C.H., Huang D.S., Zhang L., Han K. Gene expression data classification using locally linear discriminant embedding. Comput. Biol. Med. 2010, 40:802-810.
28. Norris K.H., Ritchie G.E. Assuring specificity for a multivariate near-infrared (NIR) calibration: the example of the Chambersburg Shoot-out 2002 data set. J. Pharm. Biomed. 2008, 48:1037-1041.
29. Chen Z.P., Li L.M., Yu R.Q., Littlejohn D., Nordon A., Morris J., Dann A.S., Jeffkins P.A., Richardson M.D., Stimpson S.L. Systematic prediction error correction: a noval strategy for maintaining the predictive abilities of multivariate calibration models. Analyst 2011, 136:98-106.
30. Jouan-Rimbaud D., Massart D.L., Leardi R., De Noord O.E. Genetic algorithms as a tool for wavelength selection in multivariate calibration. Anal. Chem. 1995, 67:4295-4301.
31. Buning-Pfaue H. Analysis of water in food by near infrared spectroscopy. Food Chem. 2003, 82:107-115.
32. Jiang J.H., Berry R.J., Siesler H.W., Ozaki Y. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with application to mid-infrared and near-infrared spectroscopic data. Anal. Chem. 2002, 74:3555-3565.
33. Davies A.M.C., Miller C.E. Tentative assignment of the 1440nm absorption band in the near infrared spectrum of crystalline sucrose. Appl. Spectrosc. 1988, 42:703-704.
34. Shao X.G., Leung A.K.M., Chau F.T. Wavelet: a new trend in chemistry. Acc. Chem. Res. 2003, 36:276-283.