Overview and comparative study of dimensionality reduction techniques for high dimensional data.
Shaeela AyeshaMuhammad Kashif HanifRamzan TalibPublished in: Inf. Fusion (2020)
Keyphrases
- comparative study
- high dimensional data
- dimensionality reduction
- high dimensionality
- low dimensional
- high dimensional
- high dimensions
- manifold learning
- linear discriminant analysis
- data points
- principal component analysis
- lower dimensional
- subspace clustering
- feature extraction
- original data
- similarity search
- dimension reduction
- input space
- diffusion maps
- sparse representation
- dimensionality reduction methods
- nonlinear dimensionality reduction
- nearest neighbor
- feature selection
- data representation
- feature space
- principal components
- preprocessing step
- dimensional data
- high dimensional spaces
- clustering high dimensional data
- metric learning
- pattern recognition
- computer vision
- low rank
- euclidean distance
- subspace learning
- data sets
- decision trees
- locally linear embedding
- data analysis
- kernel pca
- real world
- neural network
- intrinsic dimensionality
- small sample size
- database