Dimensionality Reduction on Heterogeneous Feature Space.
Xiaoxiao ShiPhilip S. YuPublished in: ICDM (2012)
Keyphrases
- dimensionality reduction
- feature space
- principal component analysis
- high dimensionality
- high dimensional
- linear discriminant analysis
- input space
- high dimensional data
- feature extraction
- low dimensional
- data points
- lower dimensional
- manifold learning
- feature vectors
- feature selection
- data representation
- pattern recognition
- kernel trick
- kernel pca
- kernel methods
- training samples
- pattern recognition and machine learning
- random projections
- metric learning
- principal components
- singular value decomposition
- discriminant analysis
- class separability
- mean shift
- dimensionality reduction methods
- classification accuracy
- dimension reduction
- sparse representation
- linear dimensionality reduction
- high dimensional feature space
- training set
- heterogeneous networks
- euclidean distance
- hyperplane
- intrinsic dimensionality
- image representation
- locally linear embedding
- high dimension
- embedding space
- kernel function
- support vector machine