Modeling Local Field Potentials with Regularized Matrix Data Clustering
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/655959
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AbstractIn 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.
CitationGao, X., Shen, W., Hu, J., Fortin, N., & Ombao, H. (2019). Modeling Local Field Potentials with Regularized Matrix Data Clustering. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). doi:10.1109/ner.2019.8717132
SponsorsShen’s research is partially supported by the Simons Foundation (Award 512620) and the National Science Foundation (NSF DMS 1509023).
Conference/Event name2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)