PCA REDUCED GAUSSIAN MIXTURE MODELS WITH APPLICATIONS IN SUPERRESOLUTION

PCA REDUCED GAUSSIAN MIXTURE MODELS WITH APPLICATIONS IN SUPERRESOLUTION

Despite the rapid development of computational hardware, the treatment of large and high dimensional data sets is still a challenging prob-lem. The contribution of this paper to the topic is twofold. First, we propose a Gaussian mixture model in conjunction with a reduction of the dimensionality of...

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Bibliographic Details
Main Authors: Aujol, J.-F (Author), Bernard, D. (Author), Berthoumieu, Y. (Author), Hertrich, J. (Author), Nguyen, D.-P.-L (Author), Saadaldin, A. (Author), Steidl, G. (Author)
Format: Article
Language:English
Published: American Institute of Mathematical Sciences 2022
Subjects:
Dimensionality reduction
Expectation maximization algorithm
Gaussian mixture models
Maximum likelihood estimation
Principal component analysis
Superresolution
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