Discriminative Dimensionality Reduction for the Visualization of Classifiers.
Andrej GisbrechtAlexander SchulzBarbara HammerPublished in: ICPRAM (Selected Papers) (2013)
Keyphrases
- dimensionality reduction
- feature selection
- semi supervised dimensionality reduction
- semi supervised
- feature extraction
- linear discriminant
- linear classifiers
- class discrimination
- discriminative classifiers
- unsupervised learning
- multidimensional scaling
- dimensionality reduction methods
- data representation
- high dimensionality
- high dimensional data
- object category recognition
- naive bayes
- training set
- support vector
- decision trees
- high dimensional
- subspace learning
- machine learning algorithms
- highly discriminative
- bayesian network classifiers
- data analysis
- feature set
- principal component analysis
- training data
- self organizing maps
- principal components
- low dimensional
- structure preserving
- pattern recognition and machine learning
- visualization tool
- kernel learning
- feature space
- graph embedding
- training samples
- metric learning
- linear dimensionality reduction
- pattern recognition
- discriminative information
- linear discriminant analysis
- discriminative features
- manifold learning
- lower dimensional
- data visualization
- data sets
- classifier training
- log linear models
- latent space
- semi supervised learning
- image processing