Modeling Local Field Potentials with Regularized Matrix Data Clustering

Abstract
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.

Citation
Gao, 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

Acknowledgements
Shen’s research is partially supported by the Simons Foundation (Award 512620) and the National Science Foundation (NSF DMS 1509023).

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Conference/Event Name
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

DOI
10.1109/NER.2019.8717132

Additional Links
https://ieeexplore.ieee.org/document/8717132/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8717132

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