Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the tensorial JADE in an independent component model.

1 | ```
tJADE(x, maxiter = 100, eps = 1e-06)
``` |

`x` |
Numeric array of an order at least two. It is assumed that the last dimension corresponds to the sampling units. |

`maxiter` |
Maximum number of iterations. Passed on to |

`eps` |
Convergence tolerance. Passed on to |

It is assumed that *S* is a tensor (array) of size *p_1 x p_2 x ... x p_r* with mutually independent elements and measured on *N* units. The tensor independent component model further assumes that the tensors S are mixed from each mode
*m* by the mixing matrix *A_m*, *m= 1, ..., r*, yielding the observed data *X*. In R the sample of *X* is saved as an `array`

of dimensions
*p_1, p_2, ..., p_r, N*.

`tJADE`

recovers then based on `x`

the underlying independent components *S* by estimating the *r* unmixing matrices
*W_1, ..., W_r* using fourth joint moments in a more efficient way than `tFOBI`

.

If `x`

is a matrix, that is, *r = 1*, the method reduces to JADE and the function calls `JADE`

.

For a generalization for tensor-valued time series see `tgJADE`

.

A list with class 'tbss', inheriting from class 'bss', containing the following components:

`S ` |
Array of the same size as x containing the independent components. |

`W ` |
List containing all the unmixing matrices |

`Xmu ` |
The data location. |

`datatype` |
Character string with value "iid". Relevant for |

Joni Virta

Virta, J., Li, B., Nordhausen, K. and Oja, H., (2017), JADE for Tensor-Valued Observation, to appear in Journal of Computational and Graphical Statistics. preprint available on ArXiv http://arxiv.org/abs/1603.05406.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
n <- 1000
S <- t(cbind(rexp(n)-1,
rnorm(n),
runif(n, -sqrt(3), sqrt(3)),
rt(n,5)*sqrt(0.6),
(rchisq(n,1)-1)/sqrt(2),
(rchisq(n,2)-2)/sqrt(4)))
dim(S) <- c(3, 2, n)
A1 <- matrix(rnorm(9), 3, 3)
A2 <- matrix(rnorm(4), 2, 2)
X <- tensorTransform(S, A1, 1)
X <- tensorTransform(X, A2, 2)
tjade <- tJADE(X)
MD(tjade$W[[1]], A1)
MD(tjade$W[[2]], A2)
tMD(tjade$W, list(A1, A2))
## Not run:
# Digit data example
# Running will take a few minutes
library(ElemStatLearn)
x <- zip.train
rows <- which(x[, 1] == 0 | x[, 1] == 1)
x0 <- x[rows, 2:257]
y0 <- x[rows, 1] + 1
x0 <- t(x0)
dim(x0) <- c(16, 16, 2199)
tjade <- tJADE(x0)
plot(tjade, col=y0)
## End(Not run)
``` |

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