42 search results - page 1 / 9 » Distance Approximating Dimension Reduction of Riemannian Man... |
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...
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...
ECCV
2006
Springer
14 years 6 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...
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...
SIAMMAX
2011
12 years 7 months ago
2011
Higher-order tensors are used in many application fields, such as statistics, signal processing, and scientific computing. Efficient and reliable algorithms for manipulating thes...