A4 Article in conference proceedings
Dimension Estimation in Two-Dimensional PCA (2021)

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

JYU authors or editors

Publication details

All authors or editors: Radojičić, Una; Lietzén, Niko; Nordhausen, Klaus; Virta, Joni

Parent publication: Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)

Parent publication editors: Petkovié, T.; Petrinovié, D.; Lonéarié, S.

Place and date of conference: Online/Zagreb, Croatia, 13.-15.9.2021

eISBN: 978-1-6654-2639-8

Journal or series: International Symposium on Image and Signal Processing and Analysis

ISSN: 1845-5921

eISSN: 1849-2266

Publication year: 2021

Publication date: 13/09/2021

Publisher: IEEE; University of Zagreb

Place of Publication: Zagreb

Publication country: Croatia

Publication language: English

DOI: https://doi.org/10.1109/ispa52656.2021.9552114

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78658

Abstract

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.

Free keywords: augmentation; dimension estimation; dimension reduction; image data; scree plot

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 1