Subspace Clustering of Very Sparse High-Dimensional Data.
Hankui PengNicos G. PavlidisIdris A. EckleyIoannis TsalamanisPublished in: IEEE BigData (2018)
Keyphrases
- subspace clustering
- high dimensional data
- high dimensional
- sparse representation
- dimensionality reduction
- low dimensional
- clustering high dimensional data
- nearest neighbor
- data sets
- high dimensionality
- subspace clusters
- high dimensional feature spaces
- data analysis
- similarity search
- input space
- data points
- original data
- dimension reduction
- complex data
- linear discriminant analysis
- sparse coding
- high dimensional spaces
- subspace projections
- low rank
- manifold learning
- small sample size
- clustering algorithm
- clustering method
- multi dimensional
- input data
- text data
- random projections
- dimensional data
- high dimensional datasets
- subspace clustering algorithms
- underlying manifold
- locally linear embedding
- metric space
- document clustering
- information extraction
- image data
- feature space
- image processing
- real world