Nonlinear low-dimensional regression using auxiliary coordinates.
Weiran WangMiguel Á. Carreira-PerpiñánPublished in: AISTATS (2012)
Keyphrases
- low dimensional
- locally linear
- nonlinear manifold learning
- linear dimensionality reduction
- high dimensional
- simple linear
- dimensionality reduction
- high dimensional feature space
- nonlinear regression
- low dimensional manifolds
- principal component analysis
- high dimensional data
- manifold learning
- regression model
- mercer kernels
- input space
- linear regression
- nonlinear manifold
- euclidean space
- regression problems
- feature space
- locally linear embedding
- data points
- vector space
- highly nonlinear
- linear subspace
- nonlinear functions
- appearance manifolds
- regression algorithm
- subspace learning
- regression method
- kernel pca
- component analysis
- pattern classification
- dimension reduction
- regression methods
- multidimensional scaling
- graph embedding
- neural network
- linear discriminant analysis
- pairwise
- pattern recognition
- underlying manifold
- support vector
- face recognition
- learning algorithm