The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data.
Shusen LiuPeer-Timo BremerJ. J. JayaramanBei WangBrian SummaValerio PascucciPublished in: Comput. Graph. Forum (2016)
Keyphrases
- high dimensional data
- subspace learning
- nearest neighbor
- low dimensional
- high dimensional
- dimensionality reduction
- high dimensionality
- data sets
- data points
- data analysis
- subspace clustering
- high dimensions
- manifold learning
- dimension reduction
- nonlinear dimensionality reduction
- low rank
- input space
- dimensional data
- clustering high dimensional data
- original data
- similarity search
- high dimensional spaces
- linear discriminant analysis
- high dimensional data sets
- mr images
- subspace clusters
- variable selection
- neural network
- data mining
- high dimensional datasets
- database
- learning algorithm
- feature selection
- lower dimensional
- decision trees
- pattern recognition
- image data