178 search results - page 13 / 36 » Extensions of Manifold Learning Algorithms in Kernel Feature... |
KDD
2008
ACM
14 years 8 months ago
2008
ACM
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kern...
CVPR
2008
IEEE
13 years 9 months ago
2008
IEEE
In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptu...
ICPR
2006
IEEE
14 years 8 months ago
2006
IEEE
This paper presents a novel alternative approach, namely weakly supervised learning (WSL), to learn the pre-image of a feature vector in the feature space induced by a kernel. It ...
NIPS
2008
13 years 9 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
MM
2010
ACM
13 years 7 months ago
2010
ACM
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...