Hardness and Approximability of Dimension Reduction on the Probability Simplex.
Roberto BrunoPublished in: Algorithms (2024)
Keyphrases
- dimension reduction
- principal component analysis
- high dimensional
- high dimensional data
- high dimensional problems
- approximation algorithms
- np hard
- feature extraction
- linear discriminant analysis
- cluster analysis
- singular value decomposition
- low dimensional
- variable selection
- data mining and machine learning
- high dimensionality
- partial least squares
- worst case
- manifold learning
- feature selection
- dimensionality reduction
- feature space
- manifold embedding
- random projections
- discriminative information
- unsupervised learning
- support vector machine svm
- association rules
- face recognition
- dimension reduction methods
- database
- sparse metric learning
- preprocessing
- data analysis
- pattern recognition
- image processing
- data sets