134 search results - page 7 / 27 » Random Projections for Manifold Learning |
BMVC
2010
13 years 7 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...
ALT
2007
Springer
14 years 6 months ago
2007
Springer
In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classific...
CVPR
2007
IEEE
14 years 11 months ago
2007
IEEE
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
ICCV
2009
IEEE
15 years 2 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...
ICIP
2007
IEEE
14 years 1 months ago
2007
IEEE
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...