Dimensionality Reduction via Sparse Support Vector Machines.
Jinbo BiKristin P. BennettMark J. EmbrechtsCurt M. BrenemanMinghu SongPublished in: J. Mach. Learn. Res. (2003)
Keyphrases
- dimensionality reduction
- support vector
- high dimensional
- large margin classifiers
- learning machines
- sparse representation
- linear discriminant analysis
- random projections
- reduced set
- principal component analysis
- feature selection
- kernel function
- feature space
- data representation
- principal components
- high dimensional data
- low dimensional
- high dimensionality
- support vector machine
- feature extraction
- pattern recognition
- hyperplane
- classification accuracy
- svm classifier
- manifold learning
- kernel methods
- pattern recognition and machine learning
- sparse data
- logistic regression
- loss function
- data points
- dimension reduction
- cross validation
- structure preserving
- soft margin
- maximum margin
- input space
- generalization ability
- neural network
- binary classification
- data sets
- machine learning
- nonlinear dimensionality reduction
- learning algorithm
- multi class classification
- training examples
- support vector regression
- radial basis function