5.3. FREM: fast ensembling of regularized models for robust decoding#

FREM uses an implicit spatial regularization through fast clustering and aggregates a high number of estimators trained on various splits of the training set, thus returning a very robust decoder at a lower computational cost than other spatially regularized methods. Its performance compared to usual classifiers was studied on several datasets in [Hoyos-Idrobo et al.[1]].

5.3.1. FREM pipeline#

FREM pipeline averages the coefficients of many models, each trained on a different split of the training data. For each split:

  • aggregate similar voxels together to reduce the number of features (and the computational complexity of the decoding problem). ReNA algorithm is used at this step, usually to reduce by a 10 factor the number of voxels.

  • optional : apply feature selection, an univariate statistical test on clusters to keep only the ones most informative to predict variable of interest and further lower the problem complexity.

  • find the best hyper-parameter and memorize the coefficients of this model

Then this ensemble model is used for prediction, usually yielding better and more stable predictions than a unique model at no extra-cost. Also, the resulting coefficient maps obtained tend to be more structured.

There are two object to apply FREM in Nilearn:

They can use different type of models (l2-SVM, l1-SVM, Logistic, Ridge) through the parameter ‘estimator’.

5.3.2. Empirical comparisons# Decoding performance increase on Haxby dataset#


In this example we showcase the use of FREM and the performance increase that it brings on this problem. Spatial regularization of decoding maps on mixed gambles study#


See also

  • The scikit-learn documentation has very detailed explanations on a large variety of estimators and machine learning techniques. To become better at decoding, you need to study it.

  • SpaceNet, a method promoting sparsity that can also give good brain decoding power and improved decoder maps when sparsity is important.

5.3.3. References#