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Table 3 The average recognition rates and standard deviations of different algorithms on different databases. The best results are highlighted in italics

From: Double regularized matrix factorization for image classification and clustering

Methods

Extended YaleB

CMU PIE

AR

Baseline

0.6193 ± 0.0081(1024)

0.8563 ± 0.0072(1024)

0.6206 ± 0.0162(1024)

LS

0.4850 ± 0.0142(500)

0.8196 ± 0.0180(500)

0.5851 ± 0.0155(500)

SPEC

0.6418 ± 0.0096(500)

0.8749 ± 0.0082(470)

0.6456 ± 0.0154(500)

MCFS

0.6589 ± 0.0178(200)

0.8791 ± 0.0084(490)

0.6521 ± 0.0158(500)

RUFS

0.6697 ± 0.0132(480)

0.8899 ± 0.0091(490)

0.6661 ± 0.0171(480)

MFFS

0.6722 ± 0.0093(330)

0.8862 ± 0.0088(270)

0.6741 ± 0.0147(470)

RSR

0.6883 ± 0.0106(500)

0.8937 ± 0.0085(440)

0.6671 ± 0.0147(440)

JGSC

0.6999 ± 0.0122(470)

0.8925 ± 0.0084(480)

0.6737 ± 0.0183(430)

SPNFSR

0.7190 ± 0.0077(180)

0.9184 ± 0.0103(430)

0.6895 ± 0.0117(270)

UDSFS

0.7195 ± 0.0095(490)

0.9167 ± 0.0159(400)

0.6931 ± 0.0144(240)

NSSRD

0.7202 ± 0.0119(440)

0.9185 ± 0.0096(410)

0.6979 ± 0.0152(430)

DRMFFS

0.7277 ± 0.0086(290)

0.9233 ± 0.0105(250)

0.7040 ± 0.0115(300)