Visual cluster separation using high-dimensional sharpened dimensionality reduction.
Youngjoo KimAlexandru C. TeleaScott C. TragerJos B. T. M. RoerdinkPublished in: Inf. Vis. (2022)
Keyphrases
- dimensionality reduction
- high dimensional
- data points
- low dimensional
- high dimensional data
- high dimensionality
- subspace clustering
- visual information
- data representation
- visual data
- manifold learning
- input space
- sparse data
- feature space
- variable selection
- principal component analysis
- dimension reduction
- clustering algorithm
- similarity search
- principal components
- random projections
- high dimensional spaces
- structure preserving
- low dimensional spaces
- visual features
- multi modal
- linear dimensionality reduction
- dealing with high dimensional data
- dimensionality reduction methods
- embedding space
- pattern recognition and machine learning
- feature selection
- hierarchical clustering
- low level
- pattern recognition
- multi dimensional
- feature extraction
- high level
- cluster analysis
- linear discriminant analysis
- euclidean distance
- lower dimensional
- nearest neighbor
- locally linear embedding
- nonlinear dimensionality reduction
- small sample size
- sparse coding
- training set
- data sets
- graph embedding
- support vector machine
- visual perception
- kernel function
- data clustering