Regularized matrix data clustering and its application to image analysis

Regularized matrix data clustering and its application to image analysis

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 finit...

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Bibliographic Details
Main Authors: Fortin, N.J (Author), Frostig, R.D (Author), Gao, X. (Author), Hu, J. (Author), Ombao, H. (Author), Shen, W. (Author), Zhang, L. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2021
Subjects:
accuracy assessment
algorithm
Algorithms
cluster analysis
Cluster Analysis
clustering
computer simulation
Computer Simulation
covariance analysis
data interpretation
estimation method
image analysis
image processing
Image Processing, Computer-Assisted
imaging
matrix normal distribution
mixture model
normal distribution
Normal Distribution
numerical model
prediction
regularization
time-frequency analysis
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