91 search results - page 5 / 19 » Kernel Dimensionality Reduction for Supervised Learning |
108
Voted
IJCAI
2003
14 years 11 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...
109
Voted
SAC
2006
ACM
15 years 4 months ago
2006
ACM
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
100
Voted
JMLR
2010
14 years 8 months ago
2010
Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as mani...
73
Voted
ML
2010
ACM
14 years 8 months ago
2010
ACM
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
109
Voted
PAMI
2007
14 years 9 months ago
2007
This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the proposed techniques is that they attempt to preserve both...