166 search results - page 17 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
AAAI
2012
11 years 10 months ago
2012
Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
CVPR
2007
IEEE
14 years 9 months ago
2007
IEEE
A large family of algorithms for dimensionality reduction end with solving a Trace Ratio problem in the form of arg maxW Tr(WT SpW)/Tr(WT SlW)1 , which is generally transformed in...
PR
2008
13 years 7 months ago
2008
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and...