HOSVD

# Import statement

from hottbox.algorithms.decomposition import HOSVD

class HOSVD(process=(), verbose=False)

Higher Order Singular Value Decomposition.

Parameters

processtuple

Specifies the order of modes to be processed. The factor matrices for the missing modes will be set to identity. If empty, then all modes are processed in the consecutive ascending order.

verbosebool

If True, enable verbose output

Attributes

name	Name of the decomposition

converged

Methods

copy(self)	Copy of the HOSVD algorithm as a new object
decompose(self, tensor, rank[, keep_meta])	Performs tucker decomposition via Higher Order Singular Value Decomposition (HOSVD)

plot

copy(self)

Copy of the HOSVD algorithm as a new object

decompose(self, tensor, rank, keep_meta=0)

Performs tucker decomposition via Higher Order Singular Value Decomposition (HOSVD)

Parameters

tensorTensor

Multidimensional data to be decomposed

ranktuple

Desired multilinear rank for the given tensor

keep_metaint

Keep meta information about modes of the given tensor. 0 - the output will have default values for the meta data 1 - keep only mode names 2 - keep mode names and indices

Returns

tensor_tkdTensorTKD

Tucker representation of the tensor

property name

Name of the decomposition

Returns

decomposition_namestr