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ECCV
2000
Springer
14 years 9 months ago
Non-linear Bayesian Image Modelling
In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or `subspaces', of natural images. Examples include principal component anal...
Christopher M. Bishop, John M. Winn
CVPR
2003
IEEE
14 years 9 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
PAMI
2008
200views more  PAMI 2008»
13 years 7 months ago
Principal Component Analysis Based on L1-Norm Maximization
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
Nojun Kwak
ICIP
2002
IEEE
14 years 9 months ago
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
We present a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is ext...
Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sa...
ISBI
2006
IEEE
14 years 8 months ago
Mixture principal component analysis for distribution volume parametric imaging in brain PET studies
In this paper, we present a mixture Principal Component Analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain s...
Peng Qiu, Z. Jane Wang, K. J. Ray Liu