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Non-iterative low-multilinear-rank tensor approximation with application to decomposition in rank-(1,L,L) terms

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Academic year: 2021

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Figure 1. Empirical CDFs of the times spent by each LMA algorithm at each stage.
Figure 3. Performance of BTD-(1, L, L) algorithms for i = (20,150, 150), R = 3, L = 60, SNR = 50 dB, ρ ∈ { 0, 0.2, 0.4 } and λ r ∼ N (1,0.2).
Figure 4. Performance of BTD-(1, L, L) algorithms for i = (20,150, 150), R = 3, L = 60, SNR = 50 dB, ρ ∈ { 0.2, 0.4, 0.6 } and λ r ∼ N (1,0.1).
Figure 5. Performance of BTD-(1, L, L) algorithms for i = (20, 150,150), R = 3, ρ = 0.2, λ r ∼ N (1, 0.1), L ∈ {30,90} and SNR ∈ {20, 80} dB.

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