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Table 1 Simulation designs for each scenario and corresponding true loading vectors. All true vectors are normalized to have l2-norm 1

From: Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

nen=(3,4,5)
 nel=10nel=20
σ2=1.2scenario 1scenario 2
 \(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
σ2=2.5scenario 3scenario 4
 \(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{3}, \boldsymbol {0}_{27})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{4}, \boldsymbol {0}_{36})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
nen=(5,5,5)
 nel=10nel=20
σ2=1.2scenario 5scenario 6
 \(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
σ2=2.5scenario 7scenario 8
 \(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{1}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{25})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{2}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{35})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)
 \(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{10})\)\(\boldsymbol {a}_{3}=((\boldsymbol {1}_{5}, \boldsymbol {0}_{45})^{\intercal } \bigotimes \boldsymbol {1}_{20})\)