Minimising Information Loss on Anonymised High Dimensional Data with Greedy In-Memory Processing.
Nikolai J. PodlesnyAnne V. D. M. KayemStephan von SchorlemerMatthias UflackerPublished in: DEXA (1) (2018)
Keyphrases
- high dimensional data
- information loss
- high dimensional
- dimensionality reduction
- low dimensional
- high dimensionality
- nearest neighbor
- subspace clustering
- data sets
- high dimensions
- clustering high dimensional data
- data quality
- extra information
- similarity search
- dimension reduction
- privacy protection
- data analysis
- missing values
- dimensional data
- sparse representation
- high dimensional spaces
- data points
- data distribution
- linear discriminant analysis
- lower dimensional
- nonlinear dimensionality reduction
- original data
- disclosure risk
- similarity measure
- input data
- manifold learning
- random projections
- high dimensional datasets
- image processing
- high dimensional data sets
- knn
- sample size
- anonymized data
- feature selection