Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods.
Igor GrebennikOlga ChornaInna UrniaievaPublished in: ACIT (2023)
Keyphrases
- dimensionality reduction
- dimensionality reduction methods
- high dimensional
- optimization problems
- parameter space
- low dimensional
- nonlinear dimensionality reduction
- high dimensional data
- discriminant analysis
- high dimensionality
- manifold learning
- evolutionary algorithm
- cost function
- principal component analysis
- principal components analysis
- random projections
- feature space
- locally linear embedding
- objective function
- feature extraction
- unsupervised learning
- data points
- preprocessing step
- discriminant projection
- data sets
- similarity search
- image features
- knn
- pattern recognition
- dimension reduction
- principal components
- singular value decomposition
- variable selection
- sparse representation
- machine learning
- neural network