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CVPR
2004
IEEE
16 years 1 months ago
Self-Normalized Linear Tests
Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of ...
Sachin Gangaputra, Donald Geman
CVPR
2005
IEEE
16 years 1 months ago
Local Discriminant Embedding and Its Variants
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data a...
Hwann-Tzong Chen, Huang-Wei Chang, Tyng-Luh Liu
CVPR
2005
IEEE
16 years 1 months ago
Multilinear Independent Components Analysis
Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the...
M. Alex O. Vasilescu, Demetri Terzopoulos
CVPR
2005
IEEE
16 years 1 months ago
Fisher+Kernel Criterion for Discriminant Analysis
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...
Shu Yang, Shuicheng Yan, Dong Xu, Xiaoou Tang, Cha...
CVPR
2006
IEEE
16 years 1 months ago
A Design Principle for Coarse-to-Fine Classification
Coarse-to-fine classification is an efficient way of organizing object recognition in order to accommodate a large number of possible hypotheses and to systematically exploit shar...
Sachin Gangaputra, Donald Geman