Support Vector Machines for Visualization and Dimensionality Reduction.
Tomasz MaszczykWlodzislaw DuchPublished in: ICANN (1) (2008)
Keyphrases
- dimensionality reduction
- support vector
- learning machines
- large margin classifiers
- feature selection
- multidimensional scaling
- linear discriminant analysis
- high dimensional
- low dimensional
- pattern recognition
- high dimensional data
- classification accuracy
- data representation
- principal component analysis
- logistic regression
- principal components
- high dimensionality
- input space
- multi class
- data points
- cross validation
- feature space
- kernel function
- support vector machine
- generalization ability
- interactive visualization
- data analysis
- kernel pca
- visual representation
- visualization tool
- multi class classification
- data sets
- kernel methods
- feature extraction
- nonlinear dimensionality reduction
- soft margin
- information visualization
- linear dimensionality reduction
- pattern recognition and machine learning
- manifold learning
- binary classification
- data visualization
- metric learning
- maximum margin
- svm classifier
- hyperplane
- neural network
- data mining
- learning algorithm
- structure preserving
- image processing
- graph embedding
- self organizing maps
- dimensionality reduction methods
- kernel learning
- visualization tools
- principal components analysis
- random projections