In this paper, we propose a novel regularized mixture model for clustering matrix-valued image data. The new framework introduces a sparsity structure (e.g., low rank, spatial sparsity) and separable covariance structure motivated by scientific interpretability. 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. Simulation results and analysis on brain signals show the excellent performance of the proposed method in terms of a better prediction accuracy than the competitors and the scientific interpretability of the solution.
|Title of host publication
|2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
|Institute of Electrical and Electronics Engineers (IEEE)
|Number of pages
|Published - Mar 2019
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Shen’s research is partially supported by the Simons Foundation (Award 512620) and the National Science Foundation (NSF DMS 1509023).