Discriminative common vectors for face recognition

By Hakan Cevikalp, Marian Neamtu, D. Wilkes, Atalay Barkana. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

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Eigenfeature Regularization and Extraction in Face Recognition (2008, methodology): “Eigenfeaturesare regularizeddifferentlyinthesesubspacesbasedonan eigenspectrummodel.”
Linear discriminant analysis for the small sample size problem: an overview (2014, unknown): “LDA (CLDA) [8], direct LDA (DLDA) [67], kernel DLDA (KDLDA) [28], parameterized DLDA (PDLDA) [56], improved DLDA (IDLDA) [36], pseudoinverse LDA (PILDA) [59], fast PILDA (FPILDA) [27], improved PILDA (IPILDA) [34], LDA/QR [64], approximate LDA (ALDA) [35], PCA ? LDA [5, 57], regularized LDA (RLDA) [”
2D and 3D face recognition: A survey (2007, unknown): “The DCV (Discriminant Common Vectors) (Cevikalp et al., 2005) represents a further development of this approach.”
A General Exponential Framework for Dimensionality Reduction (2014, methodology): “To tackle the SSS problem , many variants of LDA have been proposed in the recent years, such as Fisherface [3], null LDA [6], LDA/QR [7], LDA/GSVD [8], LDA/FKT [9], DLA [10], [11], Direct LDA, and its variants [6], [12]–[14].”
A two-stage linear discriminant analysis via QR-decomposition (2005, methodology): “…matrix so that the perturbed scatter matrix is positive definite and, hence, nonsingular; PCA+LDA applies an intermediate dimension reduction stage using PCA on the original data to obtain a more compact representation so that the singularity of the scatter matrix is decreased; and LDA/GSVD…”