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» Principal Component Analysis Based on L1-Norm Maximization
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ICPR
2008
IEEE
15 years 4 months ago
Face recognition using curvelet based PCA
This paper identifies a novel feature space to address the problem of human face recognition from still images. This is based on the PCA space of the features extracted by a new m...
Tanaya Mandal, Q. M. Jonathan Wu
ICANN
2005
Springer
15 years 3 months ago
Handwritten Digit Recognition with Nonlinear Fisher Discriminant Analysis
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonli...
Pietro Berkes
GECCO
2008
Springer
139views Optimization» more  GECCO 2008»
14 years 10 months ago
Coordinate change operators for genetic algorithms
This paper studies the issue of space coordinate change in genetic algorithms, based on two methods: convex quadratic approximations, and principal component analysis. In both met...
Elizabeth F. Wanner, Eduardo G. Carrano, Ricardo H...
ICIP
2009
IEEE
15 years 10 months ago
Scale-robust Feature Extraction For Face Recognition
In video surveillance, the sizes of face images are very small. However, few works have been done to investigate scalerobust face recognition. Our experiments on appearancebased m...
ECCV
2006
Springer
15 years 1 months ago
Spatial Segmentation of Temporal Texture Using Mixture Linear Models
In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single tem...
Lee Cooper, Jun Liu, Kun Huang