502 search results - page 34 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
ECIR
2008
Springer
13 years 10 months ago
2008
Springer
Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dim...
IJCNN
2006
IEEE
14 years 2 months ago
2006
IEEE
— We introduce in this paper methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. The basi...
TREC
2000
13 years 10 months ago
2000
The main goal for the Information Space system for TREC9 was early precision. To facilitate this, an emphasis was placed on seeking matches from only the TITLE, H1, H2 and H3 tags...
JMLR
2010
13 years 7 months ago
2010
A large number of learning algorithms, for example, spectral clustering, kernel Principal Components Analysis and many manifold methods are based on estimating eigenvalues and eig...
ICCV
1995
IEEE
14 years 10 days ago
1995
IEEE
A general-purpose object indexingtechnique is described that combines the virtues of principal component analysis with the favorable matching properties of high-dimensional spaces...