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» Principal Component Analysis Based on L1-Norm Maximization
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CVPR
2003
IEEE
14 years 8 months ago
Generalized Principal Component Analysis (GPCA)
This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represen...
René Vidal, Shankar Sastry, Yi Ma
CVPR
2006
IEEE
14 years 8 months ago
Selecting Principal Components in a Two-Stage LDA Algorithm
Linear Discriminant Analysis (LDA) is a well-known and important tool in pattern recognition with potential applications in many areas of research. The most famous and used formul...
Aleix M. Martínez, Manli Zhu
CVPR
2008
IEEE
14 years 8 months ago
Robust tensor factorization using R1 norm
Over the years, many tensor based algorithms, e.g. two dimensional principle component analysis (2DPCA), two dimensional singular value decomposition (2DSVD), high order SVD, have...
Heng Huang, Chris H. Q. Ding
IVC
2007
120views more  IVC 2007»
13 years 6 months ago
Modelling and segmentation of colour images in polar representations
The suitability of polar representation for quantitative image processing tasks is investigated. The classical colour polar-based representations (HLS, HSV, etc.) lead to brightne...
Jesús Angulo, Jean Serra