On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages
Oren SalzmanMichael HemmerDan HalperinPublished in: CoRR (2012)
Keyphrases
- configuration space
- high dimensional
- feature space
- lower dimensional
- training samples
- output space
- riemannian manifolds
- data sets
- dimensionality reduction
- question answering
- power consumption
- high dimension
- manifold learning
- training set
- high dimensionality
- image set
- low dimensional
- latent space
- parameter space
- pattern recognition
- small sample
- geometric structure
- euclidean space
- sample points
- natural language