543 search results - page 3 / 109 » Nonlinear principal component analysis of noisy data |
ICDM
2006
IEEE
13 years 11 months ago
2006
IEEE
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
NIPS
2008
13 years 6 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
BMCBI
2010
13 years 5 months ago
2010
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
NIPS
2007
13 years 6 months ago
2007
We propose a method for reconstruction of human brain states directly from functional neuroimaging data. The method extends the traditional multivariate regression analysis of dis...
IJCNN
2007
IEEE
13 years 11 months ago
2007
IEEE
— To approximate complex data, we propose new type of low-dimensional “principal object”: principal cubic complex. This complex is a generalization of linear and nonlinear pr...