468 search results - page 24 / 94 » Riemannian Manifold Learning |
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
BMVC
2010
13 years 5 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
CVPR
2007
IEEE
14 years 1 months ago
2007
IEEE
Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned e...
EDUTAINMENT
2007
Springer
14 years 1 months ago
2007
Springer
Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was propos...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...