Euler principal component analysis

Liwicki, Stephan and Tzimiropoulos, Georgios and Zafeiriou, Stefanos and Pantic, Maja (2013) Euler principal component analysis. International Journal of Computer Vision, 101 (3). pp. 498-518. ISSN 1573-1405

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Abstract

Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA, which we call Euler-PCA (e-PCA). In particular, our algorithm utilizes a robust dissimilarity measure based on the Euler representation of complex numbers. We show that Euler-PCA retains PCA’s desirable properties while suppressing outliers. Moreover, we formulate Euler-PCA in an incremental learning framework which allows for efficient computation. In our experiments we apply Euler-PCA to three different computer vision applications for which our method performs comparably with other state-of-the-art approaches.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/1002863
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11263-012-0558-z
Keywords: Euler PCA, Robust Subspace, Online Learning, Tracking, Background Modeling
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/s11263-012-0558-z
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Jan 2016 11:58
Last Modified: 04 May 2020 20:19
URI: http://eprints.nottingham.ac.uk/id/eprint/31427

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