DPLS and PPLS: Two PLS algorithms for large data sets

Computational Statistics and Data Analysis

Volumn 48, Issue 1, 2005, Pages 125-138

  Milidiú, Ruy L ^a   Rentería, Raúl P ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

DPLS; Large data set; NIPALS; Parallelism; PLS; PPLS

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER ARCHITECTURE; IMAGE PROCESSING; MATHEMATICAL MODELS; PARALLEL PROCESSING SYSTEMS; REGRESSION ANALYSIS;

DIRECT PARTIAL LEAST SQUARES (DPLS); DPLS; LARGE DATA SETS; NIPALS; PARALLEL PARTIAL LEAST SQUARES (PPLS); PARTIAL LEAST SQUARES (PLS); PLS; PPLS;

DATA STRUCTURES;

EID: 10144249013     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2003.10.006     Document Type: Article

References (26)

1. Borggaard, C., Thodberg, H., 1992. Optimal minimal neural interpretation of spectra. Anal. Chem. 64, 545-551.
2. Carriero, N., Gelernter, D., 1991. How to Write Parallel Programs: a First Course. MIT Press, Cambridge, MA.
3. Dalhousie, U., 1999. Three-component mixtures of metal ions data set. http://www.dal.ca/~pdwentze/index.html.
4. DeVeaux, R., Psichogios, D., Ungar, L., 1993. A comparison of two non-parametric estimation schemes: Mars and neural networks. Comput. Chem. Eng. 17, 819-837.
5. Dongarra, J.J., Hempel, R., Hey, A.J.G., Walker, D.W., 1993. A proposal for a user-level message-passing interface in a distributed memory environment. Technical Report TM-12231, Oak Ridge National Laboratory, Knoxville, TN, USA.
6. Eigenvector, R.I., 1999. http://www.eigenvector.com.
7. Foster, I., 1995. Designing and Building Parallel Programs. Addison-Wesley, Cambridge.
8. Geladi, P., Kowalski, B.R., 1986. Partial least squares regression: a tutorial. Anal. Chim. Acta 185, 1-17.
9. Golub, G.H., Loan, C.F.V., 1996. Matrix Computations. John Hopkins University Press, Baltimore, MD.
10. Haaland, D.M., Thomas, E.V., 1988. Partial least-squares methods for spectral analysis. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Anal. Chem. 60 (11), 1193-1202.
11. Ham, F.M., Kostanic, I., 1998. A neural network architecture for partial least-squares regression (plsnet) with supervised adaptive modular hebbian learning. Neural Parallel Sci. Comput. 6, 35-72.
12. Haykin, S., 1999. Neural Networks, a Comprehensive Foundation. Prentice-Hall, Englewood Cliffs, NJ.
13. Kalivas, J.H., 1997. Two data sets of near infrared spectra. Chemometrics Intelligent Lab. Systems 37, 255-259.
14. McClure, W., 1998. Software shootout at the idrc98. In: The Ninth International Diffuse Reflectance Conference, Chambersburg, Pennsylvania.
15. Milidiú, R.L., Renteria, R., 2000, Ppls: an efficient parallel algorithm for partial least-squares. In: 12th Symposium on Computer Architecture and High Performance Computing, São Pedro, SP, Brazil.
16. Milidiú, R., Renteria, R., 2001. Apls: a fast approximate algorithm for partial least-squares. In: Fifth Brazilian Conference on Neural Networks, Rio de Janeiro, Brazil.
17. Milidiú, R., Machado, R., Rentería, R., 1999. Time series forecasting through wavelets and a mixture of experts models. Neurocomputing 28, 145-156.
18. Milidiú, R., Rentería, R., de Lucena, C.J., 2001. Dpls and ppls: two pls algorithms for large data sets. In: Second International Symposium on PLS and Related Methods, Capri, Italy.
19. Morineau, A., Tenenhaus, M. (Eds.), 1999. Les Méthodes PLS, Symposium International PLS'99. Cisia-Ceresta.
20. SantaFé, I., 1992. ftp://ftp.santafe.edu/pub/time-series/ cometition/.
21. Tecator, I.A., 1995. Prediction of fat content of a meat sample. StatLib Datasets Archive.
22. Tenenhaus, M., 1998. La Régression PLS, Théorie et Pratique. Technip, Paris.
23. Wentzell, P., Andrews, D., Walsh, J., Cooley, J., Spencer, P., 1999. Estimation of hydrocarbon types in light gas oils and diesel fuels by ultraviolet absorption spectroscopy and multivariate calibration. Canad. J. Chem. 77, 391-400.
24. Wold, H., 1966. Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P. (Ed.), Multivariate Analysis II. Academic Press, New York, pp. 391-420.
25. Wold, S., Albano, C., Dunn III, W.J., Esbensen, K., Hellberg, S., Johansson, E., Sjöström, H., 1983. Pattern recognition: finding and using regularities in multivariate data. In: Martens, J. (Ed.), Proceedings of the IUFOST Conference on Food Research and Data Analysis. Applied Science Publications, London.
26. Wu, W., Massart, D., de Jong, S., 1997. The kernel pea algorithms for wide data. Part 1: theory and algorithms. Chemometrics Intelligent Lab. Systems 36, 165-172.