Dimension Estimation in Two-Dimensional PCA
Radojičić, U., Lietzén, N., Nordhausen, K., & Virta, J. (2021). Dimension Estimation in Two-Dimensional PCA. In T. Petkovié, D. Petrinovié, & S. Lonéarié (Eds.), Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021). IEEE; University of Zagreb. International Symposium on Image and Signal Processing and Analysis. https://doi.org/10.1109/ispa52656.2021.9552114
Date
2021Copyright
© 2021 IEEE
We propose an automated way of determining the optimal number of low-rank components in dimension reduction of image data. The method is based on the combination of two-dimensional principal component analysis and an augmentation estimator proposed recently in the literature. Intuitively, the main idea is to combine a scree plot with information extracted from the eigenvectors of a variation matrix. Simulation studies show that the method provides accurate estimates and a demonstration with a finger data set showcases its performance in practice.
Publisher
IEEE; University of ZagrebParent publication ISBN
978-1-6654-2639-8Conference
Is part of publication
Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)ISSN Search the Publication Forum
1845-5921Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/101409122
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Additional information about funding
The work of NL was supported by the Academy of Finland (Grant 321968). The work of JV was supported by the Academy of Finland (Grant 335077).License
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