메뉴 건너뛰기




Volumn 65, Issue 1, 2003, Pages 97-112

Projection methods in chemistry

Author keywords

Data compression; Generative Topographic Mapping; Nonlinear PCA; Projection pursuit; Visualization of data structure

Indexed keywords

ARTICLE; CHEMISTRY; ENTROPY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; TECHNIQUE;

EID: 0037469092     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(02)00107-7     Document Type: Article
Times cited : (119)

References (47)
  • 2
    • 0004020376 scopus 로고
    • Princeton Univ. Press, North Holland-Elsevier Publishers, Princeton
    • R. Bellman, Adaptive Control Process: A Guided Tour. Princeton Univ. Press, North Holland-Elsevier Publishers, Princeton, 1961.
    • (1961) Adaptive Control Process: A Guided Tour
    • Bellman, R.1
  • 4
    • 0000325341 scopus 로고
    • On lines and planes of closest fit to systems of points in space
    • Pearson K. On lines and planes of closest fit to systems of points in space. Philippine Magazine. 2(6th Series):1901;559-572.
    • (1901) Philippine Magazine , vol.2 , Issue.6TH SERIES , pp. 559-572
    • Pearson, K.1
  • 5
    • 58149421595 scopus 로고
    • Analysis of a complex of statistical variables into principals components
    • Hoteling H. Analysis of a complex of statistical variables into principals components. Journal of Educational Psychology. 24:1933;417.
    • (1933) Journal of Educational Psychology , vol.24 , pp. 417
    • Hoteling, H.1
  • 7
    • 0028318337 scopus 로고
    • Tutorial, using artificial neural networks for solving chemical problems: Part II. Kohonen self-organizing feature maps and Hopfield networks
    • Melssen W.J., Smits J.R.M., Buydens L.M.C., Kateman G. Tutorial, using artificial neural networks for solving chemical problems: Part II. Kohonen self-organizing feature maps and Hopfield networks. Chemometrics and Intelligent Laboratory Systems. 23:1994;267-291.
    • (1994) Chemometrics and Intelligent Laboratory Systems , vol.23 , pp. 267-291
    • Melssen, W.J.1    Smits, J.R.M.2    Buydens, L.M.C.3    Kateman, G.4
  • 11
    • 84887006810 scopus 로고
    • A nonlinear mapping for data structure analysis
    • Sammon J. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers. 18:1969;459-473.
    • (1969) IEEE Transactions on Computers , vol.18 , pp. 459-473
    • Sammon, J.1
  • 12
    • 0001424016 scopus 로고    scopus 로고
    • EM optimization of latent-variable density models
    • D.S. Touretzky, M.C. Mozer, Hasselmo M.E. Cambridge, MA: MIT Press
    • Bishop Ch.M., Svensén M., Williams C.K.I. EM optimization of latent-variable density models. Touretzky D.S., Mozer M.C., Hasselmo M.E. Advances in Neural Information Processing Systems. vol. 8:1996;MIT Press, Cambridge, MA.
    • (1996) Advances in Neural Information Processing Systems , vol.8
    • Bishop, Ch.M.1    Svensén, M.2    Williams, C.K.I.3
  • 20
    • 0026113980 scopus 로고
    • Nonlinear principal component analysis using autoassociative neural networks
    • Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChe Journal. 37:1991;233-243.
    • (1991) AIChe Journal , vol.37 , pp. 233-243
    • Kramer, M.A.1
  • 21
    • 0000263797 scopus 로고
    • Projection pursuit
    • Huber P.J. Projection pursuit. Annals of Statistics. 13:1985;435-475.
    • (1985) Annals of Statistics , vol.13 , pp. 435-475
    • Huber, P.J.1
  • 22
  • 25
    • 0002975747 scopus 로고
    • Towards a practical method which helps uncover the structure of a set of multivariate observations by finding the linear transformation which optimizes a new index of condensation
    • R.C. Milton, & J.A. Nelder. New York
    • Kruskal J.B. Towards a practical method which helps uncover the structure of a set of multivariate observations by finding the linear transformation which optimizes a new index of condensation. Milton R.C., Nelder J.A. Statistical Computation. 1969;. New York.
    • (1969) Statistical Computation
    • Kruskal, J.B.1
  • 27
    • 0002692783 scopus 로고
    • Soft modeling by latent variables: The non-linear iterative partial least squares (NIPALS) algorithm
    • J. Gani. London: Academic Press
    • Wold H. Soft modeling by latent variables: the non-linear iterative partial least squares (NIPALS) algorithm. Gani J. Perspectives in Probability and Statistics. 1970;403-420 Academic Press, London.
    • (1970) Perspectives in Probability and Statistics , pp. 403-420
    • Wold, H.1
  • 29
    • 0000308754 scopus 로고
    • On a heuristic method of test construction and its use in a multivariate analysis
    • Roy S.N. On a heuristic method of test construction and its use in a multivariate analysis. Annals of Mathematical Statistics. 24:1953;220-238.
    • (1953) Annals of Mathematical Statistics , vol.24 , pp. 220-238
    • Roy, S.N.1
  • 31
    • 0034235934 scopus 로고    scopus 로고
    • Sequential projection pursuit using genetic algorithms for data mining of analytical data
    • Guo Q., Wu W., Questier F., Massart D.L., Boucon C., de Jong S. Sequential projection pursuit using genetic algorithms for data mining of analytical data. Analytical Chemistry. 72:2000;2846-2855.
    • (2000) Analytical Chemistry , vol.72 , pp. 2846-2855
    • Guo, Q.1    Wu, W.2    Questier, F.3    Massart, D.L.4    Boucon, C.5    De Jong, S.6
  • 32
    • 0002593186 scopus 로고    scopus 로고
    • A fast algorithm for robust principal components based on projection pursuit
    • Heidelberg: Physica-Verlag
    • Croux C., Ruiz-Gazen A. A fast algorithm for robust principal components based on projection pursuit. COMPSTAT: Proceedings in Computational Statistics. 1996;211-217 Physica-Verlag, Heidelberg.
    • (1996) COMPSTAT: Proceedings in Computational Statistics , pp. 211-217
    • Croux, C.1    Ruiz-Gazen, A.2
  • 34
  • 35
    • 0011964375 scopus 로고
    • Indices for projection pursuit
    • I.S. Yenyukov. New York: Nova Science Publishers
    • Yenyukov I.S. Indices for projection pursuit. Data Analysis Learning Symbolic and Numeric Knowledge. 1989;181-188 Nova Science Publishers, New York.
    • (1989) Data Analysis Learning Symbolic and Numeric Knowledge , pp. 181-188
  • 36
    • 34250232348 scopus 로고
    • EM algorithms for ML factor analysis
    • Rubin D.B., Thayler D.T. EM algorithms for ML factor analysis. Psychometrika. 47:1982;69-76.
    • (1982) Psychometrika , vol.47 , pp. 69-76
    • Rubin, D.B.1    Thayler, D.T.2
  • 37
    • 84887006810 scopus 로고
    • A nonlinear mapping for data structure analysis
    • Sammon J.W. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers. 18:1969;401-409.
    • (1969) IEEE Transactions on Computers , vol.18 , pp. 401-409
    • Sammon, J.W.1
  • 39
    • 0032093910 scopus 로고    scopus 로고
    • Visualization of transformed multivariate data sets with autoassiociative neural networks
    • Aldrich C. Visualization of transformed multivariate data sets with autoassiociative neural networks. Pattern Recognition Letters. 19:1998;749-764.
    • (1998) Pattern Recognition Letters , vol.19 , pp. 749-764
    • Aldrich, C.1
  • 40
    • 0034064625 scopus 로고    scopus 로고
    • Cluster analysis of mineral process data with autoassiociative neural networks
    • Aldrich C. Cluster analysis of mineral process data with autoassiociative neural networks. Chemical Engineering Communications. 177:2000;121-137.
    • (2000) Chemical Engineering Communications , vol.177 , pp. 121-137
    • Aldrich, C.1
  • 41
    • 0035914569 scopus 로고    scopus 로고
    • Nonlinear process monitoring using bottle-neck neural networks
    • Theissen U., Melssen W.J., Buydens L.M.C. Nonlinear process monitoring using bottle-neck neural networks. Analytica Chimica Acta. 446:2001;371-383.
    • (2001) Analytica Chimica Acta , vol.446 , pp. 371-383
    • Theissen, U.1    Melssen, W.J.2    Buydens, L.M.C.3
  • 42
    • 0030221716 scopus 로고    scopus 로고
    • Plant seed classification using pyrolysis mass spectrophotometry with unsupervised learning: The application of auto-associative and Kohonen artificial neural networks
    • Goodcare R., Pygall J., Kell D.K. Plant seed classification using pyrolysis mass spectrophotometry with unsupervised learning: the application of auto-associative and Kohonen artificial neural networks. Chemometrics and Intelligent Laboratory Systems. 34:1996;69-83.
    • (1996) Chemometrics and Intelligent Laboratory Systems , vol.34 , pp. 69-83
    • Goodcare, R.1    Pygall, J.2    Kell, D.K.3
  • 43
    • 0001362410 scopus 로고
    • The Levenberg-Marquardt algorithm, implementation and theory
    • G.A. Watson. Springer
    • More J. The Levenberg-Marquardt algorithm, implementation and theory. Watson G.A. Numerical Analysis, Lecture Notes in Mathematics. 1977;630 Springer.
    • (1977) Numerical Analysis, Lecture Notes in Mathematics , pp. 630
    • More, J.1
  • 46
    • 0037201759 scopus 로고    scopus 로고
    • An evaluation of Direct Orthogonal Signal Correction and other preprocessing methods for the classification of clinical study lots of a dermatological cream
    • in press
    • J. Luypaert, S. Heuerding, S. de Jong, D.L. Massart, An evaluation of Direct Orthogonal Signal Correction and other preprocessing methods for the classification of clinical study lots of a dermatological cream, Journal of Pharmaceutical Biomedical Analysis, in press.
    • Journal of Pharmaceutical Biomedical Analysis
    • Luypaert, J.1    Heuerding, S.2    De Jong, S.3    Massart, D.L.4
  • 47
    • 0024701578 scopus 로고
    • Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra
    • Barnes R.J., Dhanoa M.S., Lister S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy. 43:1989;772-777.
    • (1989) Applied Spectroscopy , vol.43 , pp. 772-777
    • Barnes, R.J.1    Dhanoa, M.S.2    Lister, S.J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.