Research Article

Discriminative, Competitive, and Collaborative Representation-Based Classification with -Norm Regularizations

Table 4

The classification accuracies (%) of the competing methods with the corresponding standard deviations with different class-specific training samples on each UCI data set.

DatalSRCCRCCCRCProCRCEProCRCCo-CRCDSRCDCCRC

Vehicle1056.382.0062.11 ± 3.2760.02 ± 3.6060.07 ± 3.6260.02 ± 3.6253.55 ± 4.1457.94 ± 2.2163.03±3.44
1157.03 ± 1.1364.56 ± 1.7763.22 ± 1.6963.24 ± 1.6963.22 ± 1.6954.34 ± 7.0061.22 ± 4.2365.01±2.70

Auto1070.54 ± 2.2473.02 ± 2.0973.47 ± 1.6673.67 ± 1.9373.68 ± 1.9069.18 ± 5.3870.02 ± 2.8673.89±1.59
1473.80 ± 4.0675.28 ± 3.0375.66 ± 3.1475.63 ± 3.1875.62 ± 3.1771.16 ± 7.5174.16 ± 4.1375.93±3.60

Credit861.25 ± 5.8067.98 ± 2.4468.72 ± 1.0468.55 ± 0.8468.55 ± 0.8259.91 ± 5.3962.85 ± 6.8269.50±3.05
1064.90 ± 4.2868.78 ± 1.1269.25 ± 2.1969.31 ± 2.0269.22 ± 2.0161.88 ± 3.9263.82 ± 6.5972.42±2.79

Wine766.88 ± 4.7977.58 ± 6.1087.13 ± 3.1788.41 ± 2.6472.10 ± 3.6762.80 ± 3.6276.56 ± 6.1789.81±2.06
870.52 ± 5.3982.8 ± 1.9389.22 ± 4.1790.00 ± 3.0077.79 ± 3.9666.88 ± 7.0180.91 ± 6.1791.04±2.57

Heart658.76 ± 5.6961.94 ± 5.1664.65 ± 2.8964.42 ± 2.7764.42 ± 2.7761.24 ± 2.263.64 ± 8.6367.13±4.04
863.54 ± 4.1170.87 ± 3.3271.42 ± 2.6571.42 ± 2.6571.42 ± 2.6569.53 ± 5.4966.14 ± 4.4073.23±2.83

SCredit3563.10 ± 2.7771.97 ± 2.8772.00 ± 2.7772.03 ± 2.7172.23 ± 2.7757.58 ± 3.3563.74 ± 4.8175.84±3.81
5066.07 ± 3.5174.64 ± 2.8374.41 ± 1.3874.44 ± 1.3474.47 ± 1.5259.32 ± 2.6065.53 ± 4.8876.95±4.32

Isolet2171.41 ± 3.0271.38 ± 1.4869.83 ± 0.9469.47 ± 1.1069.47 ± 1.1066.36 ± 2.6972.89 ± 2.3973.19±2.50
2678.10 ± 1.7079.22 ± 2.0277.80 ± 2.2077.53 ± 2.2977.55 ± 2.3275.81 ± 3.0179.50 ± 2.2779.88±2.28

Iono1583.49 ± 4.2588.66 ± 3.8887.54 ± 3.5087.35 ± 3.3987.35 ± 3.4382.06 ± 3.8481.99 ± 3.1889.72±4.29
2086.37 ± 1.9491.38 ± 1.2788.49 ± 2.0588.04 ± 1.5587.97 ± 1.4582.06 ± 1.4583.09 ± 1.9192.99±1.12