Transductive De-Noising and Dimensionality Reduction using Total Bregman Regression.
Sreangsu AcharyyaPublished in: SDM (2006)
Keyphrases
- dimensionality reduction
- denoising
- total variation
- support vector
- label information
- image denoising
- principal component analysis
- regression model
- high dimensional
- feature selection
- manifold learning
- low dimensional
- feature extraction
- pattern recognition
- semi supervised
- pattern recognition and machine learning
- data representation
- model selection
- dimensionality reduction methods
- regression problems
- loss function
- structure preserving
- linear discriminant analysis
- wavelet domain
- image processing
- nonlinear dimensionality reduction
- unlabeled data
- kernel matrix
- text classification
- input space
- linear regression
- high dimensionality
- regression algorithm
- high dimensional data
- semi supervised learning
- support vector regression
- machine learning
- kernel learning
- least squares
- partial least squares
- convex optimization
- labeled data
- feature space
- text categorization