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Table 2 Statistical measures of performance of LMNN-based techniques in comparison with other reported methods for SPECT database

From: Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

SVM-linear classifier Accuracy (%) Sensitivity (%) Specificity(%)
VAF 83.51 83.93 82.93
PCA 86.56 91.07 80.49
GMM 89.69 90.24 89.29
Gaussian kernel PCA+LMNN Transformation 91.75 91.07 92.68
Gaussian kernel PLS+LMNN Transformation 90.72 91.07 90.24
PLS+LMNN Transformation 92.78 91.07 95.12
LMNN-RECT 80.28 70 87.80
LMNN-Classifier Accuracy (%) Euclidean Mahalanobis Energy
PCA 80.54 81.63 87.65
PLS 84.33 89.56 88.67
  1. SVM classifier: Comparison of the methods reported in this work with VAF, GMM and PCA operation points. LMNN-based techniques parameters: linear SVM classifier with 6 components. VAF parameters: linear SVM classifier, GMM parameters: σ = 6 RBF-SVM classifier with 8 components and PCA parameters: σ = 6 RBF-SVM classifier with 16 components. LMNN Classifier with 6 components: Euclidean, Mahalanobis and Energy.