166 search results - page 11 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
109
click to vote
CVPR
2006
IEEE
16 years 3 months ago
2006
IEEE
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
107
click to vote
AAAI
2010
15 years 2 months ago
2010
Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step ...
143
click to vote
PSIVT
2009
Springer
15 years 7 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...
112
click to vote
ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
ICIP
2010
IEEE
14 years 11 months ago
2010
IEEE
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality ...