Large Margin Discriminant Dimensionality Reduction in Prediction Space.
Mohammad J. SaberianJosé Costa PereiraNuno VasconcelosCan XuPublished in: NIPS (2016)
Keyphrases
- low dimensional
- dimensionality reduction
- principal component analysis
- lower dimensional
- input space
- high dimensional
- feature extraction
- high dimensional data
- higher dimensional
- data points
- data representation
- prediction accuracy
- manifold learning
- learning algorithm
- euclidean space
- class separability
- linear discriminant
- linear dimensionality reduction
- discriminant analysis
- machine learning
- support vector
- principal components
- dimensionality reduction methods
- discriminant projection
- feature selection
- locality preserving projections
- prediction algorithm
- subspace learning
- feature space
- high dimensionality
- hyperplane
- prediction model
- artificial neural networks
- locally linear embedding
- nonlinear dimensionality reduction
- prediction error
- nearest neighbor
- reduced dimensionality