Discrimination of the white wine based on sparse principal component analysis and support vector machine

Lecture Notes in Electrical Engineering

Volumn 277 LNEE, Issue , 2014, Pages 695-702

  Wang, Rong ^a   Zeng, Wu ^a   Ming, Jiao ^a  

^a WUHAN POLYTECHNIC UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

CLASSIFICATION ACCURACY; ELASTIC NET; PCA (PRINCIPAL COMPONENT ANALYSIS); SPARSE PRINCIPAL COMPONENT ANALYSIS; WHITE WINES;

SUPPORT VECTOR MACHINES;

EID: 84958532458     PISSN: 18761100     EISSN: 18761119     Source Type: Book Series    
DOI: 10.1007/978-3-319-01766-2_80     Document Type: Conference Paper

References (10)

1. Du, C., & Zhou, J. (2011). Application of infrared photoacoustic spectroscopy in soil analysis. Applied Spectroscopy Reviews, 46(5), 405-422.
2. Ying, Y., & Liu, Y. (2008). Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy. Journal of Food Engineering, 84(2), 206-213.
3. Cozzolino, D., Corbella, E., & Smyth, H. E. (2011). Quality control of honey using infrared spectroscopy: A review. Applied Spectroscopy Reviews, 46(7), 523-538.
4. Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15(2), 265-286.
5. Luss, R., & d'Aspremont, A. (2010). Clustering and feature selection using sparse principal component analysis. Optimization and Engineering, 11(1), 145-157.
6. Yu, H., Lin, H., Xu, H., et al. (2008). Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. Journal of Agricultural and Food Chemistry, 56(2), 307-313.
7. Kelly, J. W., Degenhart, A. D., Siewiorek, D. P., et al. (2012). Sparse linear regression with elastic net regularization for brain-computer interfaces. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 4275-4278). IEEE.
8. Carroll, M. K., Cecchi, G. A., Rish, I., et al. (2009). Prediction and interpretation of distributed neural activity with sparse models. Neuro Image, 44(1), 112-122.
9. Shao, Q., & Feng, C. J. (2012). Pattern recognition of chatter gestation based on hybrid PCA-SVM. Applied Mechanics and Materials, 120, 190-194.
10. Hsu, C. W., Chang, C. C., Lin, C. J. (2003). A practical guide to support vector classification. Department of Computer Science, National Taiwan University.