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.
Clone the repository using
git clone https://github.com/jpvaldes/brainowl.git
pip (be sure you have python 3):
pip install -e ./brainowl
The included Jupyter notebook contains an example usage of the BrainOwl classifier showing how to decode two categories of the classic Haxby dataset.
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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