91 search results - page 4 / 19 » Kernel Dimensionality Reduction for Supervised Learning |
ICIP
2005
IEEE
15 years 12 months ago
2005
IEEE
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...
89
Voted
ICDM
2003
IEEE
15 years 3 months ago
2003
IEEE
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
117
Voted
PSIVT
2009
Springer
15 years 4 months 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...
ICCV
2009
IEEE
16 years 3 months ago
2009
IEEE
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
106
Voted
NIPS
2008
14 years 11 months ago
2008
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...