81 search results - page 4 / 17 » Multi-Class Linear Feature Extraction by Nonlinear PCA |
PSIVT
2009
Springer
14 years 9 days ago
2009
Springer
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
NIPS
2004
13 years 7 months ago
2004
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unkno...
VLSISP
2002
13 years 5 months ago
2002
Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not...
PAMI
2008
13 years 5 months ago
2008
Beyond conventional linear and kernel-based feature extraction, we present a more generalized formulation for feature extraction in this paper. Two representative algorithms using ...
ESANN
2006
13 years 7 months ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...