Sensory motor fmri
The data set is on
/NAS_II/Projects/MVPA_Language/motorsignal
How to run the data by yourself
Go to
/NAS_II/Projects/MVPA_Language/sparse_regularization/sm_sparse
Watch the tutorial video on how to run the elastic net environment on NAS_II (it's ready for you!!!):
Result files
The results are in
/NAS_II/Projects/MVPA_Language/motorsignal/sparse_LR/
There are 21 trials, and for each trial there are 21 observation.
There are 2 types of format
1) sweeping-across-parameter for each trial
trial${trial#}_bin_lr_all_21fold${fold_option}.mat
trial#: the number of trials, can be 1 to 21
fold_option: the assignment of train, validation, test. For example, 19:1:1 --> '1119'
The result is data structure containing lambda, alpha and its corresponding train, validation and test accuracy and the voxel consistency.
2) optimal parameter for each trial
trial${trial#}_${classif_reg_type}_cri0_bin_21fold${fold_option}.mat
classif_reg_type: the classifier and the regularization type, can be 'lr_none', 'lr_elnet', 'lr_lasso', 'lr_ridge'
The result report the optimal test accuracy by selecting the best validation accuracy from the result in 1).
Experiment#1: leave-one-obs-out ---- train:validation:test = 19:1:1
classifiers: LR+elastic net, lasso, ridge and no-regularization
The summary table is here
more details report:
1) Accuracy: accuracy of train set, validation set, test set
2) Consistency: the #overlapping voxels on different number of fold, i.e. there are V voxels that are selected in F folds.
The results suggest
- elastic net regularization has the best accuracy over all trials
- elastic net reg's consistency is better than lasso's
- elastic net is more interpretable when compared with ridge or no-regularization. The size of selected features is not too big, consistent across folds and gives good accuracy