Consider the Karhunen-Loeve decomposition of the variance covariance matrix Σ as QAQT with Q=(V₁ v ... 2 Vp) and A = diag(11, 12, ..., λp), as seen in lectures. Match the following statements. tr(1) ✓ Choose... This is an identity matrix. i=1 Στη λινιντ Viv =1 This is the inverse of the variance covariance matrix. This is the total variance. This is the variance covariance matrix. VV Choose...
Consider the Karhunen-Loeve decomposition of the variance covariance matrix Σ as QAQT with Q=(V₁ v ... 2 Vp) and A = diag(11, 12, ..., λp), as seen in lectures. Match the following statements. tr(1) ✓ Choose... This is an identity matrix. i=1 Στη λινιντ Viv =1 This is the inverse of the variance covariance matrix. This is the total variance. This is the variance covariance matrix. VV Choose...
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Transcribed Image Text:Consider the Karhunen-Loeve decomposition of the variance covariance matrix Σ as QAQT with Q=(V₁ v
...
2 Vp) and A = diag(11, 12, ..., λp), as seen in lectures. Match the following statements.
tr(1)
✓ Choose...
This is an identity matrix.
i=1
Στη λινιντ
Viv
=1
This is the inverse of the variance covariance matrix.
This is the total variance.
This is the variance covariance matrix.
VV Choose...
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