Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics.
Sebastian ReiterThomas SchnappingerRegina de Vivie-RiedlePublished in: ICANN (Workshop) (2019)
Keyphrases
- dimensionality reduction
- high dimensional
- low dimensional
- dynamic model
- manifold learning
- principal component analysis
- feature space
- data representation
- pattern recognition
- high dimensional data
- high dimensionality
- neural network
- linear discriminant analysis
- feature extraction
- lower dimensional
- dimensionality reduction methods
- highly nonlinear
- random projections
- quantum computation
- principal components
- image processing
- feature selection
- input space
- discriminant analysis
- nearest neighbor
- computer vision
- social networks