Classifier tuned for neuroimaging based on SpaRSA solver
This is a classifier tuned for neuroimaging. In particular for task-related fMRI. It is meant to be used with the OWL norm (also called Ordered $l_1$ norm) and uses a solver based on SpaRSA.
The OWL norm should identify features relevant for the learning problem, even if they are correlated. Weight maps based on OWL tend to be sparse, but not so sparse like the solutions from LASSO, for example.
pip (be sure you have python3>=3.5):
pip install brainowl
If you want to have the source code, you can clone the repository using
git clone https://github.com/jpvaldes/brainowl.git
and then install it:
cd brainowl pip install -e .
The included Jupyter notebook contains an example usage of the BrainOwl classifier showing how to decode two categories of the classic neuroimaging Haxby dataset.
The dataset will be downloaded automatically if it is not found.
This project contains code from pyowl.
X Zeng, M A T Figueiredo, The Ordered Weighted $l_1$ Norm: Atomic Formulation, Projections, and Algorithms. J. Bogdan, E. Berg, W. Su, and E. Candes, Statistical Estimation and Testing via the Ordered $l_1$ Norm. Stephen Wright, Robert Nowak, and Mario Figueiredo. Sparse Reconstruction by Separable Approximation. IEEE Transactions on Signal Processing, 2009, Vol. 52, No. 7, 2479-2493. Marcos Raydan. The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization Problem. SIAM J. Optim., 1997, Vol. 7, No. 1, 26-33.
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|Filename, size & hash||File type||Python version||Upload date|
|brainowl-0.1-py3-none-any.whl (10.0 kB) View hashes||Wheel||py3|
|brainowl-0.1.tar.gz (9.2 kB) View hashes||Source||None|