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.
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 ***, 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.
| 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 |