# L1-Regularized ICA: A Novel Method for Analysis of Task-related fMRI Data

arXiv: https://arxiv.org/abs/2410.13171 · HTML: https://mlsanity.com/paper/2410.13171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Efficient DC Algorithm for Constrained Sparse Optimization](https://mlsanity.com/paper/1701.08498/) (2017)
- [Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms](https://mlsanity.com/paper/1607.00435/) (2016)
- [Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis](https://mlsanity.com/paper/2009.03077/) (2020)
- [Constrained Bayesian ICA for Brain Connectome Inference](https://mlsanity.com/paper/1911.05770/) (2019)
- [Automatic Hyperparameter Tuning in Sparse Matrix Factorization](https://mlsanity.com/paper/2305.10114/) (2023)
- [Performance evaluation of matrix factorization for fMRI data](https://mlsanity.com/paper/2312.08809/) (2023)

## Built on by (most cited first, 1 of 1)

- [Factor Decorrelation Enhanced Data Removal from Deep Predictive Models](https://mlsanity.com/paper/2509.23443/) (2025)

## Abstract (arXiv)

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.