MRS Classification Based on Independent Component Analysis and Support Vector Machines

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	Similar Articles Articles by Jian Ma Articles by Zengqi Sun

Fifth International Conference on Hybrid Intelligent Systems (HIS'05)

Rio de Janeiro, Brazil

December 06-December 09

ISBN: 0-7695-2457-5

	ASCII Text	x
	Jian Ma, Zengqi Sun, "MRS Classification Based on Independent Component Analysis and Support Vector Machines," Hybrid Intelligent Systems, International Conference on, pp. 509-511, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005.

	BibTex	x
	@article{ 10.1109/ICHIS.2005.76, author = {Jian Ma and Zengqi Sun}, title = {MRS Classification Based on Independent Component Analysis and Support Vector Machines}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2457-5}, pages = {509-511}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.76}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Hybrid Intelligent Systems, International Conference on TI - MRS Classification Based on Independent Component Analysis and Support Vector Machines SN - 0-7695-2457-5 SP509 EP511 A1 - Jian Ma, A1 - Zengqi Sun, PY - 2005 KW - Support vector machines KW - independent component analysis KW - magnetic resonance spectra KW - classification KW - feature extraction VL - 0 JA - Hybrid Intelligent Systems, International Conference on ER -

Jian Ma, Tsinghua Univ., Beijing China

Zengqi Sun, Tsinghua Univ., Beijing China

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.76

A novel scheme is proposed in this paper which combines Independent Component Analysis (ICA) and Support Vector Machines (SVM) to classify MRS. ICA is used to extract features by decomposing MRS into components which correspond to biomedical metabolites. SVM is used to train a classifier based on features extracted by ICA. The new scheme can extract meaningful features and therefore obtain a classifier with good generalization. Experimental results show that the new method has better performance than others previous ones.

Index Terms:

Support vector machines, independent component analysis, magnetic resonance spectra, classification, feature extraction

Citation:

Jian Ma, Zengqi Sun, "MRS Classification Based on Independent Component Analysis and Support Vector Machines," his, pp.509-511, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005

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