Dimensionality Reduction of Movement Primitives in Parameter Space.
Samuele TosattoJonas StadtmuellerJan PetersPublished in: CoRR (2020)
Keyphrases
- parameter space
- dimensionality reduction
- high dimensional
- high dimensional data
- low dimensional
- high dimensionality
- data representation
- starting points
- multivariate data
- feature extraction
- image space
- feature space
- parameter values
- structure preserving
- high level
- dimensionality reduction methods
- principal component analysis
- data points
- low level
- search space
- pattern recognition
- principal components
- linear discriminant analysis
- manifold learning
- pattern recognition and machine learning
- kernel pca
- lower dimensional
- trajectory data
- riemannian manifolds
- building blocks
- feature selection
- graph embedding
- lie group
- computer vision
- hough space
- neural network
- euclidean distance
- metric learning
- training samples
- search algorithm
- target function