PCA Feature Selection For Recognition of Chatter Gestation

Qiang Shao; Chang Jian Feng; Jing Kang

doi:10.4028/www.scientific.net/AMM.239-240.434

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240PCA Feature Selection For Recognition of Chatter...

PCA Feature Selection For Recognition of Chatter Gestation

Abstract:

A SVM(Support Vector Machine)-like framework provides a novel way to learn linear principal component analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features is introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on chatter gestation. The results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

434-437

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.434

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Online since:

December 2012

Authors:

Qiang Shao, Chang Jian Feng, Jing Kang

Keywords:

Chatter Gestation, Pattern Recognition, Principal Component Analysis (PCA), Support Vector Machine (SVM)

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References

[1] Gao Hong-li: The Investigation of Intelligent Tool Wear Monitoring Techniques for Metal Cutting Process，Ph.D. Southwest Jiaotong University，China, Sep. (2005)

Google Scholar

[2] Wang Wei：Research on Too1 Condition Monitoring and on-line Compensation Technology in Milling Special Spiral Rod，Ph.D. Northeast University，Feb.(2006)

Google Scholar

[3] Lu-Hsien Chen,Gwo-Fong Lin,Chen-Wang Hsu.Development of Design Hyetographs for Ungauged Sites Using an Approach Combining PCA, SOM and Kriging Methods, Water Resour Manage,2011(2):1269-1291.

DOI: 10.1007/s11269-011-9791-4

Google Scholar

[4] Rui Zhang · Wenjian Wang. Learning Linear and Nonlinear PCA with Linear Programming. Neural Process Lett (33):151–170,2011.

DOI: 10.1007/s11063-011-9170-4

Google Scholar

[5] Liao, L.-Z., Luo, S.-W., & Tian, M. "Whitenedfaces" recognition with PCA and ICA. IEEE Signal Processing Letters, 14(12), 1008–1011,2007.

DOI: 10.1109/lsp.2007.904704

Google Scholar

[6] Zhao, W., Chellapa, R., Rosenfeld, A., & Phillips, P. J. Face recognition: A literature survey. ACM Computing Surveys:399–458,2003.

DOI: 10.1145/954339.954342

Google Scholar

[7] Vapnik, V. N. Statistical learning theory. New York:Wiley. 1998.

Google Scholar

[8] Barakat N, Diederich J (2005) Eclectic rule-extraction from support vector machines. Int J Comput Intell 2(1):59–62.

Google Scholar