Standardizes an array of array-valued observations simultaneously from each mode. The method can be seen as a higher-order analogy for the regular multivariate standardization of random vectors.

1 |

`x` |
Array of an order higher than two with the last dimension corresponding to the sampling units. |

The algorithm first centers the *n* observed tensors *Xi* to have an element-wise mean of zero. Then it estimates the *m*th mode covariance matrix *Cov_m(X) = E(X(m) X(m)^T)/(p1 ... p(m-1) p(m+1) ... pr)*, where *X(m)* is the centered *m*-flattening of *X*, for each mode and transforms the observations with the inverse square roots of the covariance matrices from the corresponding modes.

A list containing the following components:

`x ` |
Array of the same size as |

`S ` |
List containing inverse square roots of the covariance matrices of different modes. |

Joni Virta

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# Generate sample data.
n <- 100
x <- t(cbind(rnorm(n, mean = 0),
rnorm(n, mean = 1),
rnorm(n, mean = 2),
rnorm(n, mean = 3),
rnorm(n, mean = 4),
rnorm(n, mean = 5)))
dim(x) <- c(3, 2, n)
# Standardize
z <- tensorStandardize(x)$x
# The m-mode covariance matrices of the standardized tensors
mModeCovariance(z, 1)
mModeCovariance(z, 2)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.