1313 search results - page 22 / 263 » Dimensionality Reduction for Classification |
201
click to vote
VLDB
2007
ACM
16 years 2 months ago
2007
ACM
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
158
click to vote
CVPR
2009
IEEE
16 years 9 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 ...
152
click to vote
ICCV
2007
IEEE
16 years 3 months ago
2007
IEEE
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
115
click to vote
ICML
2009
IEEE
16 years 2 months ago
2009
IEEE
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
137
click to vote
MM
2010
ACM
15 years 2 months ago
2010
ACM
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...