(6) MVSE : MSVE is an initially proposed multiview algorithm for dimensionality reduction.
(1) As can be seen from Tables 1, 2, and 3 and Figures 4, 5, and 6, our proposed MDGPP algorithm consistently outperforms the conventional single-view-based algorithms (i.e., PCA, LDA, LPP, MFA, and DGPP) and the latest multiview algorithms (i.e., MVSE, MSNE, MSE, and SSMVE) in all the experiments, which implies that extracting a discriminative feature subspace by using both intraclass geometry and interclass discrimination and explicitly considering the complementary information of different facial features can achieve the best recognition performance.
(2) The multiview learning algorithms (i.e., MVSE, MSNE, MSE, SSMVE, and MDGPP) perform much better than single-view-based algorithms (i.e., PCA, LDA, LPP, MFA, and DGPP), which demonstrates that simple concatenation strategy cannot duly combine features from multiple views, and the recognition performance can be successfully improved by exploring the complementary characteristics of different views.
(4) For the multiview learning algorithms, the supervised multiview algorithms (i.e., MSE and MDGPP) outperform the unsupervised multiview algorithms (i.e., MVSE, MSNE, and SSMVE) due to the utilization of the labeled facial images.
(5) Although MVSE, MSNE, and SSMVE are all unsupervised multiview learning algorithms, SSMVE performs much better than MVSE and MSNE.