ArticleBiometrics2021
Regularized matrix data clustering and its application to image analysis.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- TIME-VARYINGData science in science · 2024Article
- Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.Biometrics · 2023Article
- A Penalized Matrix Normal Mixture Model for Clustering Matrix Data.Entropy (Basel, Switzerland) · 2021Article
- HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data.Journal of neuroscience methods · 2020Article
- Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials.Statistica Sinica · 2020Article
- A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.Econometrics and statistics · 2020Article
- Matrix Linear Discriminant Analysis.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2020Article
- Covariate-adjusted region-referenced generalized functional linear model for EEG data.Statistics in medicine · 2019Article
- A hierarchical independent component analysis model for longitudinal neuroimaging studies.NeuroImage · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (eg, low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an expectation maximization type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.