Detection of orthogonal concepts in subspaces of high dimensional data.
Stephan GünnemannEmmanuel MüllerInes FärberThomas SeidlPublished in: CIKM (2009)
Keyphrases
- high dimensional data
- high dimensional
- nearest neighbor
- low dimensional
- dimensionality reduction
- data sets
- high dimensions
- data points
- high dimensionality
- subspace clustering
- original data
- data distribution
- similarity search
- input space
- nonlinear dimensionality reduction
- high dimensional datasets
- data analysis
- detection algorithm
- lower dimensional
- clustering high dimensional data
- dimension reduction
- low rank
- linear discriminant analysis
- manifold learning
- text data
- neural network
- high dimensional data sets
- knn
- real world
- subspace learning
- database
- sparse representation
- principal component analysis