This is basically the Tucker decomposition of a K-Tensor,
tucker, with one of the modes uncompressed. If K = 3, then this is also known as the Generalized Low Rank Approximation of Matrices (GLRAM). This implementation assumes that the last mode is the measurement mode and hence uncompressed. This is an iterative algorithm, with two possible stopping conditions: either relative error in Frobenius norm has gotten below
tol, or the
max_iter number of iterations has been reached. For more details on the MPCA of tensors, consult Lu et al. (2008).
Tensor with K modes
a vector of the compressed modes of the output core Tensor, this has length K-1
maximum number of iterations if error stays above
relative Frobenius norm error tolerance
Uses the Alternating Least Squares (ALS) estimation procedure. A progress bar is included to help monitor operations on large tensors.
a list containing the following:
the extended core tensor, with the first K-1 modes given by
a list of K-1 orthgonal factor matrices - one for each compressed mode, with the number of columns of the matrices given by
whether or not
tol by the last iteration
tnsr after compression
the percent of Frobenius norm explained by the approximation
the Frobenius norm of the error
vector containing the Frobenius norm of error for all the iterations
The length of
ranks must match
H. Lu, K. Plataniotis, A. Venetsanopoulos, "Mpca: Multilinear principal component analysis of tensor objects". IEEE Trans. Neural networks, 2008.
1 2 3 4 5
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.