Low-dimensional feature extraction for humanoid locomotion using kernel dimension reduction.
Jun MorimotoSang-Ho HyonChristopher G. AtkesonGordon ChengPublished in: ICRA (2008)
Keyphrases
- dimension reduction
- low dimensional
- feature space
- feature extraction
- high dimensional
- principal component analysis
- input space
- dimensionality reduction
- manifold learning
- principle component analysis
- high dimensional data
- kernel function
- kernel pca
- graph embedding
- feature representation
- euclidean space
- data points
- random projections
- kernel methods
- kernel matrix
- embedding space
- latent space
- high dimensional feature space
- high dimensionality
- subspace learning
- linear subspace
- lower dimensional
- nonlinear dimensionality reduction
- feature selection
- support vector
- linear discriminant analysis
- feature vectors
- metric learning
- humanoid robot
- face recognition
- preprocessing
- intrinsic dimension
- cluster analysis
- feature set
- pattern recognition
- training data
- computer vision