On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages.
Oren SalzmanMichael HemmerDan HalperinPublished in: IEEE Trans Autom. Sci. Eng. (2015)
Keyphrases
- configuration space
- high dimensional
- feature space
- riemannian manifolds
- lower dimensional
- training samples
- low dimensional
- output space
- dimensionality reduction
- sample set
- power consumption
- high dimensionality
- euclidean space
- high dimension
- manifold learning
- degrees of freedom
- high dimensional spaces
- question answering
- data samples
- small sample
- sample points
- embedding space
- learning algorithm
- latent space
- document retrieval
- optimal configuration
- feature selection