88 search results - page 4 / 18 » Distance Preserving Dimension Reduction for Manifold Learnin... |
IVC
2007
13 years 8 months ago
2007
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
CORR
2011
Springer
13 years 3 months ago
2011
Springer
—We consider the problem of positioning a cloud of points in the Euclidean space Rd , from noisy measurements of a subset of pairwise distances. This task has applications in var...
KDD
2007
ACM
14 years 9 months ago
2007
ACM
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
ICPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
ECCV
2006
Springer
14 years 10 months ago
2006
Springer
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...