Endo Y, Takeda K. ℓ 1 -Regularized ICA: A Novel Method for Analysis of Task-Related fMRI Data.
Neural Comput 2024;
36:2540-2570. [PMID:
39312486 DOI:
10.1162/neco_a_01709]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/17/2024] [Indexed: 09/25/2024]
Abstract
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, 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 a 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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