Explainable Structuring and Discovery of Relevant Cases for Exploration of High-Dimensional Data.
Joris FalipFrédéric BlanchardMichel HerbinPublished in: IUI Workshops (2019)
Keyphrases
- high dimensional data
- dimensionality reduction
- nearest neighbor
- high dimensional
- low dimensional
- high dimensions
- high dimensionality
- data points
- data sets
- subspace clustering
- data analysis
- complex data
- manifold learning
- data distribution
- linear discriminant analysis
- dimension reduction
- dimensional data
- original data
- clustering high dimensional data
- lower dimensional
- input space
- small sample size
- nonlinear dimensionality reduction
- similarity search
- high dimensional feature spaces
- gene expression data
- text data
- feature extraction
- high dimensional spaces
- subspace learning
- high dimensional data sets
- knowledge discovery
- pattern recognition
- low dimensional structure
- locally linear embedding
- variable selection
- data streams
- feature selection
- machine learning
- data mining