Dimensionality reduction by unsupervised regression.
Miguel Á. Carreira-PerpiñánZhengdong LuPublished in: CVPR (2008)
Keyphrases
- dimensionality reduction
- unsupervised learning
- discriminant projection
- regression model
- high dimensional data
- high dimensionality
- principal component analysis
- low dimensional
- data representation
- data points
- feature extraction
- manifold learning
- high dimensional
- unsupervised feature selection
- linear dimensionality reduction
- semi supervised
- feature selection
- support vector
- pattern recognition
- feature space
- principal components
- unsupervised manner
- subspace learning
- supervised classification
- regression analysis
- nonlinear dimensionality reduction
- supervised learning
- data driven
- dimension reduction
- polynomial regression
- locally weighted
- regression algorithm
- metric learning
- random projections
- dimensionality reduction methods
- structure preserving
- neural network
- pattern recognition and machine learning
- model selection
- simple linear
- kernel pca
- support vector regression
- input space
- linear discriminant analysis
- multidimensional scaling
- data analysis
- artificial neural networks
- text classification