141 search results - page 8 / 29 » Grouping and dimensionality reduction by locally linear embe... |
CVPR
2009
IEEE
15 years 2 months ago
2009
IEEE
Non-linear dimensionality reductionmethods are powerful techniques to deal with
high-dimensional datasets. However, they often are susceptible to local minima
and perform poorly ...
CVPR
2007
IEEE
14 years 9 months ago
2007
IEEE
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...
CVPR
2005
IEEE
14 years 9 months ago
2005
IEEE
In the last decades, a large family of algorithms supervised or unsupervised; stemming from statistic or geometry theory have been proposed to provide different solutions to the p...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
SAC
2005
ACM
14 years 1 months ago
2005
ACM
Video and image datasets can often be described by a small number of parameters, even though each image usually consists of hundreds or thousands of pixels. This observation is of...