Regularized matrix data clustering and its application to image analysis
Type
ArticleAuthors
Gao, Xu
Shen, Weining

Zhang, Liwen
Hu, Jianhua

Fortin, Norbert J.
Frostig, Ron D.
Ombao, Hernando

KAUST Department
Biostatistics GroupComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program
Date
2020-08-24Embargo End Date
2021-07-21Submitted Date
2019-08-14Permanent link to this record
http://hdl.handle.net/10754/664640
Metadata
Show full item recordAbstract
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 (e.g., 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 EM-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.Citation
Gao, X., Shen, W., Zhang, L., Hu, J., Fortin, N. J., Frostig, R. D., & Ombao, H. (2020). Regularized matrix data clustering and its application to image analysis. Biometrics. doi:10.1111/biom.13354Sponsors
The authors thank the editor, the associate editor and two referees for their constructive and helpful comments on the earlier version of this paper. Shen’s research was partially supported by the Simons Foundation Award 512620 and the NSF Grant DMS 1509023. Hu’s effort was partially supported by the National Institute of Health Grants R01AI143886, R01CA219896,and CCSG P30 CA013696. Fortin’s research was supported by NIH grant R01-MH115697,NSF awards IOS-1150292 and BCS-1439267, and Whitehall Foundation award 2010-05-84.Frostig’s research was partially supported by the Leducq Foundation.Publisher
WileyJournal
BiometricsarXiv
1808.01749Additional Links
https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13354ae974a485f413a2113503eed53cd6c53
10.1111/biom.13354