Table 1.
Notations.
Fig 1.
The framework of proposed 2D-NLRSC.
Table 2.
Statistics of the image datasets in the experiments.
Table 3.
Experimental results on ORL and JAFFE datasets.
The parameters are set as ,
,
on ORL;
,
,
on JAFFE.
Table 4.
Experimental results on CMU-PIE and Yale datasets.
The parameters are set as ,
,
on CMU-PIE;
,
,
on Yale.
Table 5.
Experimental results on MNIST dataset (L = 3 and L = 5).
The parameters are set as ,
,
on MNIST.
Table 6.
Experimental results on MNIST dataset (L = 8 and L = 10).
The parameters are set as ,
,
on MNIST.
Fig 2.
Comparison of ACC and NMI for Different Algorithms On COIL-20 Dataset.
Fig 3.
Ablation study of the proposed method: Comparisons on dataset (A) CMU-PIE and dataset (B) Yale.
Fig 4.
The performance of 2D-NLRSC on the ORL and Yale datasets under different K values.
Table 7.
Performance comparison of 2D-NLRSC under different initializations on ORL and Yale datasets.
Fig 5.
ACC and NMI of the 2D-NLRSC method on ORL and Yale datasets: Dependence on parameters r,p,q and ((A),(E): ORL-r,p; (B),(F): Yale-r,p; (C),(G): ORL-
; (D),(H): Yale-
).
Fig 6.
ACC and NMI of the 2D-NLRSC method on JAFFE, CMU-PIE, Yale and MNIST (L = 10) datasets: Dependence on parameters and
((A),(E): JAFFE; (B),(F): CMU-PIE; (C),(G): Yale; (D),(H): MNIST (L = 10)).
Table 8.
Running time (sec) comparison on different datasets.
Fig 7.
Error convergence curves of 2D-NLRSC on different datasets ((A): ORL; (B): JAFFE; (C): CMU-PIE; (D): Yale; (E): MNIST (L = 3); (F): MNIST (L = 5)).