88 search results - page 1 / 18 » Distance Preserving Dimension Reduction for Manifold Learnin... |
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
TSMC
2010
12 years 11 months ago
2010
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
IJON
2010
13 years 3 months ago
2010
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensiona...
PAMI
2008
13 years 4 months ago
2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...