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Table 3 Comparison of maximum performance across processing components

From: Improving clustering performance using independent component analysis and unsupervised feature learning

Method

Performance of applied processing (NMI, ACC)

L2

L2, ICA

L2, RICA

L2, RICA, ICA

L2, SFT

L2, SFT, ICA

COIL20

0.918, 0.857b

0.92, 0.856b

0.929, 0.894b

0.914, 0.885b

0.965, 0.93 b

0.946, 0.912a

COIL100

0.914, 0.774b

0.914, 0.784a

0.943, 0.813b

0.962, 0.897 a

0.932, 0.765b

0.954, 0.849a

CMU-PIE

0.941, 0.85b

0.986, 0.937 c

0.816, 0.716b

0.848, 0.721d

0.844, 0.759b

0.866, 0.774b

USPS

0.845, 0.828a

0.854, 0.81a

0.868, 0.926 a

0.85, 0.794e

0.852, 0.817b

0.853, 0.813b

MNIST

0.774, 0.787a

0.824, 0.882 b

0.79, 0.828b

0.787, 0.822b

0.79, 0.824b

0.794, 0.853a

REUTERS-10K

0.446, 0.656c

0.46, 0.714 c

    
  1. Both NMI and ACC are presented in each cell, where the first value is the NMI. Italic font within a row indicates the maximum performance obtained for a dataset. The clustering algorithm providing the maximum performance for a given processing component and dataset is indicated by the following symbols: aGNMF; bSPC-SYM; cPCA; dSPC-RW; eICA-SYM