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 *** (left singular vectors) are orthogonal, the columns of *** (right singular vectors) are orthogonal and *** is a diagonal matrix 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.
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 *** (left singular vectors) are orthogonal, the columns of *** (right singular vectors) are orthogonal and *** is a diagonal matrix 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 *** (left singular vectors) are orthogonal, the columns of *** (right singular vectors) are orthogonal and *** is a diagonal matrix 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.
status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||
scheduled repetition interval | last repetition or drill |