502 search results - page 48 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
CAEPIA
2003
Springer
14 years 2 months ago
2003
Springer
A new method for ensemble generation is presented. It is based on grouping the attributes in dierent subgroups, and to apply, for each group, an axis rotation, using Principal Com...
JIIS
2002
13 years 8 months ago
2002
Our research aims at discovering useful knowledge from the large collection of satellite images of typhoons using data mining approaches. We first introduce the creation of the typ...
BCI
2009
IEEE
14 years 3 months ago
2009
IEEE
—In this paper, we describe and compare three Collaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implemen...
MM
2004
ACM
14 years 2 months ago
2004
ACM
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
CORR
2010
Springer
13 years 8 months ago
2010
Springer
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from th...