MRS classification based on independent component analysis and support vector machines
Jian Ma, Zengqi Sun · 2005
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.