The idea of randomized low-rank matrix approximations is to restrict the high-dimensional input data matrix to a low-dimensional space. In plain words, the aim is to find a smaller matrix which captures the essential information of the input matrix. This smaller matrix can then be used to extract (learn) the coherent structure of the data. Probabilistic algorithms considerably reduce the computational demands of traditional (deterministic) algorithms, and the computational advantage becomes pronounced with increasing matrix dimensions.
The Python software library ristretto provides a collection of randomized matrix algorithms which can be used for dimension reduction. Overview of implemented routines:
from ristretto.svd import compute_rsvd.
from ristretto.interp_decomp import compute_rinterp_decomp.
from ristretto.cur import compute_rcur.
from ristretto.lu import compute_rlu.
from ristretto.nmf import compute_rnmf_fhals.
To get the latest stable and development versions of ristretto run:
$ git clone https://github.com/erichson/ristretto
Then, to build and install the package, run from within the main directory in the release:
$ python setup.py install
Note you will need the following 3rd party packages installed in your environment:
After successfully installing the ristretto library, the unit tests can be run by:
$ python setup.py test