88 search results - page 5 / 18 » Distance Preserving Dimension Reduction for Manifold Learnin... |
CVPR
2006
IEEE
14 years 10 months ago
2006
IEEE
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
ISBI
2008
IEEE
14 years 9 months ago
2008
IEEE
We describe a new approach for elucidating the nonlinear degrees of freedom in a distribution of shapes depicted in digital images. By combining a deformation-based method for mea...
MLDM
2005
Springer
14 years 2 months ago
2005
Springer
In this paper we describe a new cluster model which is based on the concept of linear manifolds. The method identifies subsets of the data which are embedded in arbitrary oriented...
SIGIR
2005
ACM
14 years 2 months ago
2005
ACM
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from...
CVPR
2007
IEEE
14 years 10 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...