Robust dimensionality reduction via feature space to feature space distance metric learning.
Bo LiZhang-Tao FanXiao-Long ZhangDe-Shuang HuangPublished in: Neural Networks (2019)
Keyphrases
- feature space
- dimensionality reduction
- metric learning
- distance metric learning
- high dimensionality
- high dimensional
- nonlinear dimensionality reduction
- principal component analysis
- low dimensional
- input space
- feature extraction
- data points
- linear discriminant analysis
- feature vectors
- manifold learning
- kernel function
- high dimensional data
- distance metric
- kernel pca
- pattern recognition
- mean shift
- input data
- dimension reduction
- data representation
- feature selection
- image representation
- data sets
- nearest neighbor classification
- image processing
- image classification
- dimensionality reduction methods
- kernel methods
- preprocessing step
- semi supervised
- locally linear embedding
- pairwise
- random projections
- nearest neighbor
- hyperplane
- principal components
- training samples
- sparse representation