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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 ***) are orthogonal, the columns of $V$ (right singular ***) are orthogonal and $D$ is a diagonal matrix of singular ***.

Answer

[default - edit me]

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 ***) are orthogonal, the columns of $V$ (right singular ***) are orthogonal and $D$ is a diagonal matrix of singular ***.

Answer

?

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 ***) are orthogonal, the columns of $V$ (right singular ***) are orthogonal and $D$ is a diagonal matrix of singular ***.

Answer

[default - edit me]

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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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