Utilizing differential characteristics of high dimensional data as a mechanism for dimensionality reduction.
Samuel S. XingMd Tauhidul IslamPublished in: Pattern Recognit. Lett. (2022)
Keyphrases
- high dimensional data
- dimensionality reduction
- low dimensional
- high dimensionality
- high dimensional
- high dimensions
- pattern recognition
- data points
- principal component analysis
- manifold learning
- linear discriminant analysis
- nearest neighbor
- dimension reduction
- input space
- subspace learning
- subspace clustering
- data analysis
- similarity search
- sparse representation
- feature extraction
- nonlinear dimensionality reduction
- data sets
- lower dimensional
- data representation
- dimensionality reduction methods
- feature space
- feature selection
- original data
- principal components
- random projections
- dimensional data
- euclidean distance
- low rank
- high dimensional spaces
- image processing
- intrinsic dimensionality
- clustering high dimensional data
- variable weighting