Euler Principal Component Analysis

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, wh...

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Bibliographic Details
Published in:International journal of computer vision 2013-02, Vol.101 (3), p.498-518
Main Authors: Liwicki, Stephan, Tzimiropoulos, Georgios, Zafeiriou, Stefanos, Pantic, Maja
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Computational efficiency
Computer Imaging
Computer Science
Computer vision
Distance learning
Image Processing and Computer Vision
Image processing systems
International
Learning
Machine vision
Methods
Optimization
Optimization techniques
Pattern Recognition
Pattern Recognition and Graphics
Principal component analysis
Principal components analysis
Representations
Retarding
Studies
Vision
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