A Sparse Grid Based Generative Topographic Mapping for the Dimensionality Reduction of High-Dimensional Data.
Michael GriebelAlexander HullmannPublished in: HPSC (2012)
Keyphrases
- high dimensional data
- dimensionality reduction
- generative topographic mapping
- low dimensional
- high dimensional
- dimension reduction
- manifold learning
- sparse representation
- random projections
- high dimensionality
- similarity search
- data points
- principal component analysis
- linear discriminant analysis
- nonlinear dimensionality reduction
- unsupervised learning
- feature space
- lower dimensional
- high dimensional spaces
- pattern recognition
- dimensional data
- feature selection
- underlying manifold
- euclidean space
- nearest neighbor
- dimensionality reduction methods
- feature extraction
- subspace clustering
- data clustering
- singular value decomposition
- geodesic distance
- locally linear embedding
- visual data
- query processing
- euclidean distance
- data analysis
- training data
- image segmentation
- image data
- semi supervised
- vector space
- maximum likelihood
- multi dimensional