Application of dimensionality reduction to visualisation of high-throughput data and building of a classification model in formulated consumer product design

Chemical Engineering Research and Design

Volumn 90, Issue 12, 2012, Pages 2179-2185

  Peremezhney, Nicolai ^a   Connaughton, Colm ^a   Unali, Gianfranco ^b   Hines, Evor ^a   Lapkin, Alexei A ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNILEVER RESEARCH   (United Kingdom)

Author keywords

Classification; Formulated products; S Isomap; System properties, Dimensionality reduction

Indexed keywords

CLASSIFICATION MODELS; DATA SETS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; FORMULATED PRODUCTS; HIGH THROUGHPUT EXPERIMENTS; HIGH-THROUGHPUT DATA; INTRINSIC STRUCTURES; ISOMETRIC FEATURE MAPPING; K-MEANS CLUSTERING ALGORITHM; K-NEAREST NEIGHBOUR (K-NN); S-ISOMAP;

CLASSIFICATION (OF INFORMATION); PRODUCT DESIGN; VISUALIZATION;

CONSUMER PRODUCTS;

EID: 84870250637     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2012.05.010     Document Type: Article

References (21)

1. Borg I., Groenen P. Modern Multidimensional Scaling: Theory and Application 1997, Springer-Verlag, Berlin Heidelberg New York.
2. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees 1984, CRC Press, Boca Raton, FL.
3. Colbourn E.A., Rowe R.C. Advanced neural computing software systems: data mining in processing and formulation. Pharm. Technol. 2006, Available at (last accessed 30.04.2011). http://pharmtech.findpharma.com/pharmtech/article/articleDetail.jsp%3Fid=384577%26sk=%26date=%26pageID=3.
4. Fisher R. The use of multiple measurements in taxonomic problems. Ann. Eugen. 1936, 7:179-188.
5. Geng X., De-Chuan Z., Zhi-Hua Z. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2005, 35:1098-1107.
6. Ho T.K. Nearest neighbours in random subspaces. Lecture notes in computer Science: Advances in Pattern Recognition. 1998, 1451:640-948.
7. Jolliffe I.T. Principal Component Analysis 2002, Springer, New York.
8. Kaufman L., Rousseeuw P. Finding Groups in Data: An Introduction to Cluster Analysis 1990, Wiley, New York.
9. Maaten, L., 2010. Matlab toolbox for dimensionality reduction. Available at: (last accessed 5.04.2011). http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html.
10. Nie F., Xu D., Li X., Xiang S. Semi-supervised dimensionality reduction and classification through virtual label regression. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2011, 41(3):675-685.
11. Rosenblatt F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms 1961, Spartan Books, Washington DC.
12. Rowe R.C., Colbourn E.A. Neural computing in product formulation. Chem. Educ. 2003, 8:1-8.
13. Rowe R.C., Roberts R.J. Artificial intelligence in pharmaceutical product formulation: neural computing and emerging technologies. Pharm. Sci. Technol. Today 1998, 1:200-205.
14. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
15. Seber G.A.F. Multivariate Observations 1984, John Wiley & Sons, Inc, Hoboken, NJ.
16. Shlens, J., 2005. A Tutorial on Principal Component Analysis. Institute for Nonlinear Science, UCSD. Available at: Last accessed: 7th April 2011. http://www.snl.salk.edu/~shlens/pub/notes/pca.pdf.
17. Tenenbaum J.B., Silva V.De., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
18. Theodoridis S., Konstantinos K. Pattern Recognition 2008, Academic Press. fourth ed.
19. Vapnik V. Statistical Learning Theory 1998, John Wiley and Sons, New York.
20. Wasserman P.D. Advanced Methods in Neural Computing 1993, Van Nostrand Reinhold, New York.
21. Zhang Z., Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 2004, 26:313-338.