Biometrics: Theory, Methods, and Applications (IEEE Press by N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia
By N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou
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XM } is available. Each tensor object Xm ∈ RI1 ×I2 ×···×IN assumes values in the tensor space RI1 ⊗ RI2 · · · ⊗ RIN , where In is the n-mode dimension of the tensor. The objective of MLDA-TPP is to find a multilinear mapping {U(n) ∈ RIn ×Pn , n = 1, . . , N} from the original tensor space RI1 ⊗ RI2 · · · ⊗ RIN into a tensor subspace RP1 ⊗ RP2 . . ⊗ RPN (with Pn < In , for n = 1, . . , N): T T T Ym = Xm ×1 U(1) ×2 U(2) · · · ×N U(N) , m = 1, . . 15) based on the optimization of a certain separation criterion, such that an enhanced separability between different classes is achieved.