208 search results - page 16 / 42 » Robust Kernel Principal Component Analysis |
PAMI
2002
13 years 7 months ago
2002
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
BMCBI
2007
13 years 7 months ago
2007
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
ICIC
2009
Springer
14 years 2 months ago
2009
Springer
Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis ...
ICCV
2007
IEEE
14 years 1 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
ICCV
2009
IEEE
13 years 5 months ago
2009
IEEE
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...