Parametric dimensionality reduction by unsupervised regression.
Miguel Á. Carreira-PerpiñánZhengdong LuPublished in: CVPR (2010)
Keyphrases
- dimensionality reduction
- unsupervised learning
- discriminant projection
- regression model
- principal component analysis
- data representation
- high dimensional
- semi parametric
- high dimensionality
- feature extraction
- data driven
- low dimensional
- supervised learning
- high dimensional data
- regression problems
- subspace learning
- input space
- supervised classification
- regression algorithm
- unsupervised feature selection
- principal components
- structure preserving
- linear dimensionality reduction
- linear discriminant analysis
- pattern recognition and machine learning
- simple linear
- dimensionality reduction methods
- feature selection
- regression analysis
- semi supervised
- data points
- linear regression
- feature space
- pattern recognition
- locally weighted
- ridge regression
- nonlinear dimensionality reduction
- random projections
- gaussian processes
- unsupervised manner
- manifold learning
- partial least squares
- model selection
- regression methods
- regression method
- support vector regression
- dimension reduction
- kernel pca