166 search results - page 1 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
ICML
2004
IEEE
13 years 10 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
IJCAI
2003
13 years 6 months ago
2003
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
ICIP
2005
IEEE
14 years 6 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...
CVPR
2007
IEEE
14 years 6 months ago
2007
IEEE
Kernel machines (e.g. SVM, KLDA) have shown state-ofthe-art performance in several visual classification tasks. The classification performance of kernel machines greatly depends o...
ICPR
2006
IEEE
14 years 5 months ago
2006
IEEE
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...