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Found 5 papers, 1 papers with code
Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis
Independent vector analysis (IVA) generalizes ICA to multiple datasets, i. e., to multi-subject data, and in addition to higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity.
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning
In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups.
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.
Independent Component Analysis by Entropy Maximization with Kernels
ICA algorithms cast in the ML framework often deviate from its theoretical optimality properties due to poor estimation of the source PDF.
Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis
Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent.
