L1-Regularized ICA: A Novel Method for Analysis of Task-related fMRI Data
This is an incremental improvement for researchers analyzing task-related fMRI data to enhance feature interpretability.
The authors tackled the problem of interpretability in independent component analysis (ICA) for high-dimensional data by proposing an L1-regularized ICA method, which they validated on synthetic and real fMRI data.
We propose a new method of independent component analysis (ICA) in order to extract appropriate features from high-dimensional data. In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted features. For the improvement of interpretability, it is considered that sparse constraint on a factorized matrix is helpful. With this background, we construct a new ICA method with sparsity. In our method, the L1-regularization term is added to the cost function of ICA, and minimization of the cost function is performed by difference of convex functions algorithm. For the validity of our proposed method, we apply it to synthetic data and real functional magnetic resonance imaging data.