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Table 7 Classification results of A2, AG, MM for the training set, varying the number of extracted features.

From: Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

PC/SS

LTE. PCA

LTE. Convex-NMF

STE. PCA

STE. Convex-NMF

2

Total:68.4% ± 3.4

Total:62.2% ± 3.6

Total:83.7% ± 2.6

Total:80.6% ± 2.7

 

A2:75.0% ± 9.8

A2:55.4% ± 11.7

A2:86.8% ± 7.6

A2:78.0% ± 8.9

 

AG:65.9% ± 4.1

AG:71.6% ± 4.2

AG:82.9% ± 3.3

AG:84.0% ± 3.3

 

MM:71.0% ± 6.1

MM:45.7% ± 7.0

MM:84.3% ± 4.9

MM:74.3% ± 5.7

3

Total:77.6% ± 3.0

Total:73.0% ± 3.2

Total:81.7% ± 2.8

Total:83.4% ± 2.6

 

A2: 95.0% ± 4.8

A2:90.2% ± 6.8

A2:85.7% ± 7.6

A2:95.4% ± 4.4

 

AG: 71.5% ± 4.3

AG:63.6% ± 4.6

AG:81.3% ± 3.5

AG:81.7% ± 3.6

 

MM: 83.5% ± 4.9

MM:85.4% ± 5.0

MM:80.8% ± 5.3

MM:82.7% ± 4.9

4

Total:80.2% ± 2.9

Total:79.4% ± 3.0

Total:81.3% ± 2.7

Total:87.7% ± 2.3

 

A2:100% ± 0.0

A2:94.9% ± 5.2

A2:90.7% ± 6.4

A2:95.5% ± 4.6

 

AG:75.9% ± 4.2

AG:72.5% ± 4.3

AG:80.6% ± 3.5

AG:86.3% ± 3.2

 

MM:81.6% ± 5.2

MM:87.5% ± 4.3

MM:79.2% ± 5.3

MM:87.7% ± 4.3

5

Total:83.6% ± 2.7

Total:82.2% ± 2.9

Total:81.8% ± 2.7

Total:86.3% ± 2.4

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:90.8% ± 6.3

A2:90.6% ± 6.4

 

AG:79.7% ± 3.9

AG:80.0% ± 3.9

AG:80.5% ± 3.6

AG:86.3% ± 3.1

 

MM:85.5% ± 4.6

MM:80.3% ± 5.4

MM:81.2% ± 5.3

MM:84.6% ± 4.6

6

Total:84.8% ± 2.5

Total:84.9% ± 2.6

Total:92.1% ± 1.9

Total:91.8% ± 1.9

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:95.3% ± 4.6

A2:95.7% ± 4.1

 

AG:81.7% ± 3.5

AG:82.8% ± 3.6

AG:92.7% ± 2.3

AG:91.2% ± 2.6

 

MM:85.4% ± 4.8

MM:83.7% ± 4.9

MM:89.7% ± 4.1

MM:91.4 ± 3.8

7

Total:84.0% ± 2.7

Total:83.2% ± 2.7

Total:92.5% ± 1.9

Total:92.3% ± 1.9

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:95.6% ± 4.4

A2:91.2% ± 6.1

 

AG:80.6% ± 3.8

AG:79.0% ± 4.0

AG:92.6% ± 2.3

AG:92.1% ± 2.4

 

MM:85.2% ± 4.8

MM:85.7% ± 4.6

MM:91.2% ± 3.9

MM:93.0% ± 3.4

8

Total:83.0% ± 2.7

Total:85.3% ± 2.7

Total:93.5% ± 1.7

Total:92.2% ± 1.9

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:95.3% ± 4.6

A2:95.6% ± 4.5

 

AG:78.7% ± 3.8

AG:80.7% ± 3.8

AG:93.0% ± 2.2

AG:91.2% ± 2.5

 

MM:85.4% ± 4.9

MM:89.0% ± 4.3

MM:92.9% ± 3.4

MM:93.2% ± 3.3

9

Total:84.3% ± 2.6

Total:85.3% ± 2.6

Total:93.5% ± 1.7

Total:94.2% ± 1.7

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:95.3% ± 4.6

A2:95.5% ± 4.6

 

AG:80.7% ± 3.6

AG:82.6% ± 3.5

AG:93.5% ± 2.2

AG:95.2% ± 1.9

 

MM:85.5% ± 4.7

MM:85.5% ± 4.8

MM:92.9% ± 3.4

MM:91.4% ± 3.7

10

Total:82.7% ± 2.8

Total:88.4% ± 2.3

Total:92.6% ± 1.9

Total:93.7% ± 1.7

 

A2:100% ± 0.0

A2:100% ± 0.0

A2:95.5% ± 4.5

A2:95.7% ± 4.5

 

AG:79.1% ± 3.9

AG:87.0% ± 3.2

AG:91.9% ± 2.5

AG:93.5% ± 2.2

 

MM:83.6% ± 4.9

MM:87.1% ± 4.5

MM:93.1% ± 3.4

MM:93.1% ± 3.3

  1. Classification results (accuracy ± standard deviation) for the training set, at LTE and STE, obtained when varying the number of extracted features (principal components -PC- and source signals -SS-) from 4 to 10, for the problem A2, AG, MM. Fisher LDA was the classification method, and results were validated through bootstrap. The second and fourth columns show the results for PCA, and the third and fifth columns the results for Convex-NMF