On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages.
Oren SalzmanMichael HemmerDan HalperinPublished in: WAFR (2012)
Keyphrases
- configuration space
- high dimensional
- feature space
- lower dimensional
- riemannian manifolds
- output space
- dimensionality reduction
- high dimension
- low dimensional
- training samples
- power consumption
- data sets
- manifold learning
- small sample
- intrinsic dimension
- sample points
- high dimensionality
- question answering
- high dimensional data
- training set
- intrinsic dimensionality
- optimal configuration
- degrees of freedom
- document retrieval
- shape analysis
- image set
- hilbert space
- principal component analysis
- knn
- lower dimension