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tensor_decomposition.rst
``````Tensor decomposition
====================

One of the most great features of tensors is that they can be represented compactly in decomposed form and we have powerful methods with guarantees to obtain these decomposition.

In this tutorial we will go over these decomposed forms and how to perform tensor decomposition.

Kruskal form of a tensor
------------------------

The idea is to express the tensor as a sum of rank one tensors. That is, a sum of outer product of vectors.
Such representation can be obtained by applying Canonical Polyadic Decomposition (also known as CANDECOMP-PARAFAC, CP, or PARAFAC decomposition).

CANDECOMP-PARAFAC decomposition
+++++++++++++++++++++++++++++++

We demonstrate here how to perform a Canonical Polyadic Decomposition. A rank-r Parafac decomposes a tensor into a linear combination of r rank-1 tensors (See [1]_ for more details).

First, let's create a second order tensor that is zero everywhere except in a swiss shape that is one.

.. code-block::python
>>> import numpy as np
>>> import tensorly as tl
>>> tensor = tl.tensor([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.],
[ 0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.],
[ 0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.],
[ 0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])

We will now apply a rank-2 CANDECOMP-PARAFAC (:func:`tensorly.decomposition.parafac`) decomposition on `tensor`
to decompose this into a kruskal tensor.

A Parafac decompositions expresses the tensor as a kruskal tensor that can be represented as a list of factors (matrices).
The :func:`parafac` function therefore returns a list of factors.

.. code::

>>> from tensorly.decomposition import parafac
>>> factors = parafac(tensor, rank=2)
>>> len(factors)
2
>>> [f.shape for f in factors]
[(12, 2), (12, 2)]

From this **kruskal tensor** (presented as a list of matrices) you can reconstruct a full tensor:

.. code::

>>> print(tl.kruskal_to_tensor(factors))
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.]
[ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.]
[ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.]
[ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  1.  1.  1.  1.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]

Tucker form of a tensor
-----------------------

The Tucker decomposition can be seen as a generalisation of the CP decomposition: it decomposes the tensor into a small core tensor and factor matrices. CP can be seen as a Tucker decomposition with a super-diagonal core.

A tensor in its decomposed Tucker form is therefore nothing more than a core tensor with the same order as the original tensor and a list of projection matrices, one for each mode of the core tensor.

Tucker decomposition
+++++++++++++++++++++

Tucker (classical and non-negative) are available in TensorLy (:func:`tensorly.decomposition.tucker` and :func:`tensorly.decomposition.non_negative_tucker`).

Using the same tensor as previously, we will perform a rank [2, 3]-decomposition of `tensor`:

.. code::

>>> from tensorly.decomposition import tucker
>>> core, factors = tucker(tensor, ranks=[2, 3])
# The core is a smaller tensor of size (2, 3):
>>> core.shape
(2, 3)
>>> len(factors)
2
>>> [f.shape for f in factors]
[(12, 2), (12, 3)]

As previously, we can reconstruct a full tensor from our Tucker decomposition:

.. code:: python

>>> from tensorly import tucker_to_tensor
>>> print(tucker_to_tensor(core, factors)
[[  0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[  7.746e-17   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   0.000e+00]
[  7.746e-17   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   0.000e+00]
[  7.746e-17   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   0.000e+00]
[  7.746e-17   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   1.000e+00   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[ -7.340e-17   2.617e-16   1.914e-16   2.475e-16   1.000e+00   1.000e+00   1.000e+00   1.000e+00   2.475e-16   2.475e-16   2.475e-16   0.000e+00]
[  0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00   0.000e+00]]

Note that some coefficients are almost zero (10e-16) but not exactly due to numerical approximations.

References
----------
.. [1] T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications",
SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.

``````