Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization
DOI10.1016/j.neunet.2021.05.016zbMath1526.92007arXiv2010.09941OpenAlexW3163839018MaRDI QIDQ6079078
Tomoki Tokuda, Okito Yamashita, Junichiro Yoshimoto
Publication date: 28 September 2023
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.09941
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural networks for/in biological studies, artificial life and related topics (92B20)
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Cites Work
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- A well-conditioned estimator for large-dimensional covariance matrices
- A tutorial on Bayesian nonparametric models
- A framework to uncover multiple alternative clusterings
- A Riemannian framework for tensor computing
- Learning latent block structure in weighted networks
- Machine Learning for Signal Processing
- A New Implementation of k-MLE for Mixture Modeling of Wishart Distributions
- Positive Definite Matrices
- Optimal Whitening and Decorrelation
- Variational inference for Dirichlet process mixtures
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