通过L1正则化提升脑功能MRI数据的独立成分可解释性
L1-Regularized ICA: A Novel Method for Analysis of Task-related fMRI Data
- 在ICA代价函数中加入L1正则项，增强特征稀疏性
- 在模拟与真实fMRI数据上均验证了方法有效性
- 适合关注脑信号可解释性的神经影像研究者
我们提出一种新的独立成分分析（ICA）方法，用于从高维数据中提取合适特征。传统矩阵分解方法如ICA存在特征可解释性差的问题。为改善这一问题，考虑对分解矩阵施加稀疏约束。基于此，我们构建了一种带有稀疏性的新ICA方法：在ICA的代价函数中引入L1正则化项，并采用凸函数之差算法（DC algorithm）进行最小化求解。为验证所提方法的有效性，我们将其应用于合成数据和真实功能性磁共振成像（fMRI）数据，结果表明该方法能有效提升特征的稀疏性和可解释性。
原文摘要 · Abstract (English)
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.
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