Supervised Manifold Learning for NIR Modeling of Cigarette Brand Identification

Ying He; Xiang Qian Ding; Lin Tao Ma

doi:10.4028/www.scientific.net/AMR.457-458.1258

Paper Titles

Anti-Detection Simulation Study in Thruster Motor System of Underwater Robot Based on Anti-Control of Chaos
p.1235

The Security and Application of the RFID-Based Vehicle Security Monitoring and Administration System of the Internet of Things
p.1243

Optical Properties of Near-Infrared Cyanine Dye Molecules Adsorbed on Silver Nanoparticles
p.1250

An Algorithm of early Smoke Detection Based on Multiple Features
p.1254

Supervised Manifold Learning for NIR Modeling of Cigarette Brand Identification
p.1258

Research on the Lander Stability during Lunar Rover Unloading Base on the Theory of ZMP
p.1264

Filtering Method for Pose Angles Based on CS-STF-IMM
p.1271

On Process of Local Higher Education Information in China
p.1278

SBA-15(P)-Supported Tetraethylenepentamine for CO₂Capture
p.1283

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 457-458Supervised Manifold Learning for NIR Modeling of...

Supervised Manifold Learning for NIR Modeling of Cigarette Brand Identification

Abstract:

For NIR data has the character of high dimension, nonlinear, and high noise, we often confront the problem of dimensionality reduction when building the classification model on Near-Infrared spectra data. Traditional classification methods and linear dimensionality reduction techniques are difficult to improve the model performance. In this paper, a novel nonlinear modeling for NIR spectra analysis was proposed by combining S-Isomap and KNN. S-Isomap is a supervised manifold learning method which can effectively find out the intrinsic low dimensional structure and extract important information from the raw data. Compared with KLLE, KPCA, and other classification methods such as SVM or LDA, the results show that S-Isomap-KNN method performs the best on the modeling of cigarette brand identification. The method is also a good technique for visualization.

You might also be interested in these eBooks

Advanced Materials and Engineering Materials

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 457-458)

Pages:

1258-1263

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.457-458.1258

Citation:

Cite this paper

Online since:

January 2012

Authors:

Ying He, Xiang Qian Ding, Lin Tao Ma

Keywords:

Cigarette Band Identification, Dimensionality Reduction, KNN, Near-Infrared Spectra, S-ISOMAP, Supervised Manifold Learning

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] B.G. Osborne, T. Fearn: Near Infrared Spectroscopy in Food Analysis (John. Wiley & Sons, New York, USA 1986).

Google Scholar

[2] Y.L. Yan, L.L. Zhao, D.H. Han: Foundation of Near-Infrared Spectral Analysis and its application (China Light Industry Press, Beijing, China 2005).

Google Scholar

[3] S.T. Roweis, L.K. Saul: Nonlinear dimensionality reduction by local linear embedding. Science Vol. 290 (2000), pp.2323-2326.

DOI: 10.1126/science.290.5500.2323

Google Scholar

[4] J.B. Tenenbaum, V.D. Silva annd J.C. Langford V: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science. Vol. 290 (2000), pp.2319-2323.

DOI: 10.1126/science.290.5500.2319

Google Scholar

[5] J.A. Lee, A. Lendasse and M. Verleysen: Nonlinear Projection with Curvilinear Distances: Isomap versus Curvilinear Distance Analysis. Neurocomputing. Vol. 57(2004), pp.49-76.

DOI: 10.1016/j.neucom.2004.01.007

Google Scholar

[6] G, Xin, D.C. Zhan and Z.H. Zhou: Supervised Nonlinear Dimensionality Reduction for Visualization and Classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics. Vol. 35, No. 6 (2005), pp.1098-1107.

DOI: 10.1109/tsmcb.2005.850151

Google Scholar

[7] G. Shakhnarovich, T. Darrell and P. Indyk: Nearest-Neighbor Methods in Learning and Vision (The MIT Press, USA 2006).

DOI: 10.7551/mitpress/4908.001.0001

Google Scholar

[8] R.J. Liu, Y. H Wang, T. Baba and D, Masumoto: Semi-supervised Learning by Locally Linear Embedding in Kernel Space. Pattern Recognition, ICPR 2008, 19th International Conference. pp.1-4.

DOI: 10.1109/icpr.2008.4761127

Google Scholar

[9] B. Schölkopf, A.J. Smola, K.R. Müller: Kernel Principal Component Analysis. ICANN, Proceedings of the 7th International Conference on Artificial Neural Networks (1997), pp.583-588.

DOI: 10.1007/bfb0020217

Google Scholar