Interpolation-based k-means Clustering Improvement for Sparse, High Dimensional Data.
Wanghu ChenZhen TianPublished in: ICCBDC (2019)
Keyphrases
- high dimensional data
- high dimensional
- sparse representation
- dimensionality reduction
- low dimensional
- high dimensionality
- nearest neighbor
- high dimensions
- high dimension
- data sets
- similarity search
- data analysis
- original data
- dimension reduction
- data distribution
- subspace clustering
- input space
- low rank
- subspace learning
- manifold learning
- clustering high dimensional data
- data points
- text data
- sparse coding
- high dimensional feature spaces
- linear discriminant analysis
- k means
- variable selection
- random projections
- high dimensional spaces
- feature space
- lower dimensional
- underlying manifold
- small sample size
- machine learning
- nonlinear dimensionality reduction
- image processing
- training data
- dimensional data
- pattern recognition