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Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are ***, the columns of $V$ (right singular vectors) are *** and $D$ is a *** of singular values.

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are orthogonal, the columns of $V$ (right singular vectors) are orthogonal and $D$ is a diagonal matrix of singular values.

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are ***, the columns of $V$ (right singular vectors) are *** and $D$ is a *** of singular values.

?

Question

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are ***, the columns of $V$ (right singular vectors) are *** and $D$ is a *** of singular values.

If X is a matrix with each variable in a column and each observation in a row,

then the SVD is a matrix decomposition that represents X as a matrix product of three matrices:

X = U DV ′

where the columns of U (left singular vectors) are orthogonal, the columns of $V$ (right singular vectors) are orthogonal and $D$ is a diagonal matrix of singular values.

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#### pdf

owner: aelizzybeth - (no access) - Exploratory_Data_Analysis_with_R_Peng.pdf, p134

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