Two-dimensional discriminant transform for face recognition

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Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, methodology): “PCA and LDA are two well-known linear subspace learning methods which have been extensively used in pattern recognition and computer vision areas and have become the most popular techniques for face recognition and other biometrics [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [ 13 ], [1”
Feature Selection Using Principal Component Analysis (2010, methodology): “We used the ORL [18], AR [19] and Feret [20] face databases to test our method.”
Ensembles for feature selection: A review and future trends (2019, neutral): “As in the case of microarrays, (cid:12)lter feature selection methods have been the most popular, followed by classi(cid:12)cation ensembles, as in the works carried out in [122, 123, 124].”
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, neutral): “Ç”